{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c72d57c3",
   "metadata": {},
   "source": [
    "# IMPORT LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f861754e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "libraries loaded!\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.colors as mplclr\n",
    "from matplotlib.ticker import (MultipleLocator)\n",
    "import copy as cp\n",
    "import itertools\n",
    "    \n",
    "    \n",
    "mpl.rcParams['figure.figsize'] = (10,10)\n",
    "mpl.rcParams['xtick.direction'] = 'in'\n",
    "mpl.rcParams['ytick.direction'] = 'in'\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "mpl.rcParams['mathtext.rm'] = 'serif'\n",
    "\n",
    "print('libraries loaded!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d79d83",
   "metadata": {},
   "source": [
    "# Loading common variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba8c1a1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Workind Directory is  /home/alessio/Dropbox/progetti-literature_past-uoc/progetto-gender-ainamar/code\n",
      "Input Directory is /home/alessio/Dropbox/progetti-literature_past-uoc/progetto-gender-ainamar/results/\n",
      "Output Directory is /home/alessio/Dropbox/progetti-literature_past-uoc/progetto-gender-ainamar/results/\n",
      "Common variables loaded!\n"
     ]
    }
   ],
   "source": [
    "# working directory\n",
    "WD = os.getcwd()\n",
    "\n",
    "print('Workind Directory is ', WD)\n",
    "\n",
    "# input directory\n",
    "indirname = WD[:-4]+'results/'\n",
    "\n",
    "print('Input Directory is', indirname)\n",
    "\n",
    "# output directory\n",
    "diroutname = WD[:-4]+'results/'\n",
    "\n",
    "print('Output Directory is', diroutname)\n",
    "\n",
    "\n",
    "# RGB custom color definition\n",
    "# (from https://coolors.co/8ecae6-219ebc-023047-ffb703-fb8500)\n",
    "\n",
    "mycustom_colors = {\"mycol1\":\"#8ecae6\",\\\n",
    "                   \"mycol2\":\"#219ebc\",\\\n",
    "                   \"mycol3\":\"#023047\",\\\n",
    "                   \"mycol4\":\"#ffb703\",\\\n",
    "                   \"mycol5\":\"#fb8500\"}\n",
    "\n",
    "# RGB custom color definition\n",
    "# (from https://coolors.co/003049-6b2c39-d62828-e75414-f77f00-fcbf49)\n",
    "\n",
    "mycustom_colors2 = {\"mycol21\":\"#003049\",\\\n",
    "                    \"mycol22\":\"#6b2c39\",\\\n",
    "                    \"mycol23\":\"#d62828\",\\\n",
    "                    \"mycol24\":\"#e75414\",\\\n",
    "                    \"mycol25\":\"#f77f00\",\\\n",
    "                    \"mycol26\":\"#fcbf49\"}\n",
    "\n",
    "\n",
    "# Definisco i colori per men, women, unknown\n",
    "\n",
    "mycolor_men = mycustom_colors['mycol3']\n",
    "mycolor_women = mycustom_colors['mycol5']\n",
    "mycolor_unkno = mycustom_colors['mycol1']\n",
    "\n",
    "\n",
    "# number of random replicas of the random case\n",
    "nr_replicas = 50\n",
    "\n",
    "# list of graph labels\n",
    "label_list = ['REL', 'COR', 'CB', 'PUB', 'EV']\n",
    "\n",
    "nr_graphs = len(label_list)\n",
    "\n",
    "print('Common variables loaded!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada25c46",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5bf67378",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Functions loaded!\n"
     ]
    }
   ],
   "source": [
    "def heatmap(data, row_labels, col_labels, ax=None,\n",
    "            cbar_kw={}, cbarlabel=\"\", **kwargs):\n",
    "    \"\"\"\n",
    "    Create a heatmap from a numpy array and two lists of labels.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data\n",
    "        A 2D numpy array of shape (M, N).\n",
    "    row_labels\n",
    "        A list or array of length M with the labels for the rows.\n",
    "    col_labels\n",
    "        A list or array of length N with the labels for the columns.\n",
    "    ax\n",
    "        A `matplotlib.axes.Axes` instance to which the heatmap is plotted.  If\n",
    "        not provided, use current axes or create a new one.  Optional.\n",
    "    cbar_kw\n",
    "        A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.\n",
    "    cbarlabel\n",
    "        The label for the colorbar.  Optional.\n",
    "    **kwargs\n",
    "        All other arguments are forwarded to `imshow`.\n",
    "    \"\"\"\n",
    "\n",
    "    if not ax:\n",
    "        ax = plt.gca()\n",
    "\n",
    "    # applying mask to array\n",
    "    mymask =  np.tri(data.shape[0], k=-1.)\n",
    "    data = np.ma.array(data, mask=mymask)\n",
    "        \n",
    "    # Plot the heatmap\n",
    "    im = ax.imshow(data, **kwargs)\n",
    "\n",
    "    # Create colorbar\n",
    "    cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n",
    "    cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n",
    "\n",
    "    # Show all ticks and label them with the respective list entries.\n",
    "    ax.set_xticks(np.arange(data.shape[1]), labels=col_labels)\n",
    "    ax.set_yticks(np.arange(data.shape[0]), labels=row_labels)\n",
    "\n",
    "    # Let the horizontal axes labeling appear on top.\n",
    "    ax.tick_params(top=True, bottom=False,\n",
    "                   labeltop=True, labelbottom=False)\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), ha=\"center\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    # Turn spines off and create white grid.\n",
    "    ax.spines[:].set_visible(False)\n",
    "\n",
    "    ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n",
    "    ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n",
    "    ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=4)\n",
    "    ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "\n",
    "    return im, cbar\n",
    "\n",
    "\n",
    "def annotate_heatmap(im, data=None, valfmt=\"{x:.2f}\",\n",
    "                     textcolors=(\"black\", \"white\"),\n",
    "                     threshold=None, **textkw):\n",
    "    \"\"\"\n",
    "    A function to annotate a heatmap.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    im\n",
    "        The AxesImage to be labeled.\n",
    "    data\n",
    "        Data used to annotate.  If None, the image's data is used.  Optional.\n",
    "    valfmt\n",
    "        The format of the annotations inside the heatmap.  This should either\n",
    "        use the string format method, e.g. \"$ {x:.2f}\", or be a\n",
    "        `matplotlib.ticker.Formatter`.  Optional.\n",
    "    textcolors\n",
    "        A pair of colors.  The first is used for values below a threshold,\n",
    "        the second for those above.  Optional.\n",
    "    threshold\n",
    "        Value in data units according to which the colors from textcolors are\n",
    "        applied.  If None (the default) uses the middle of the colormap as\n",
    "        separation.  Optional.\n",
    "    **kwargs\n",
    "        All other arguments are forwarded to each call to `text` used to create\n",
    "        the text labels.\n",
    "    \"\"\"\n",
    "\n",
    "    if not isinstance(data, (list, np.ndarray)):\n",
    "        data = im.get_array()\n",
    "\n",
    "    # Normalize the threshold to the images color range.\n",
    "    if threshold is not None:\n",
    "        threshold = im.norm(threshold)\n",
    "    else:\n",
    "        threshold = im.norm(data.max())/2.\n",
    "\n",
    "    # Set default alignment to center, but allow it to be\n",
    "    # overwritten by textkw.\n",
    "    kw = dict(horizontalalignment=\"center\",\n",
    "              verticalalignment=\"center\")\n",
    "    kw.update(textkw)\n",
    "\n",
    "    # Get the formatter in case a string is supplied\n",
    "    if isinstance(valfmt, str):\n",
    "        valfmt = mpl.ticker.StrMethodFormatter(valfmt)\n",
    "\n",
    "    # Loop over the data and create a `Text` for each \"pixel\".\n",
    "    # Change the text's color depending on the data.\n",
    "    texts = []\n",
    "    for i in range(data.shape[0]):\n",
    "        for j in range(data.shape[1]):\n",
    "            if j > i:\n",
    "                kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])\n",
    "                text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)\n",
    "                texts.append(text)\n",
    "            elif j == i:\n",
    "                kw.update(color='snow')\n",
    "                text = im.axes.text(j, i, valfmt(1., None), **kw)\n",
    "                texts.append(text)\n",
    "            else:\n",
    "                kw.update(color='white')\n",
    "                text = im.axes.text(j, i, '')\n",
    "                texts.append(text)\n",
    "\n",
    "                \n",
    "    return texts\n",
    "\n",
    "print('Functions loaded!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d38e1fe",
   "metadata": {},
   "source": [
    "# Single layer network viz\n",
    "\n",
    "## Visualization of gender distribution across k-cores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0467374d",
   "metadata": {},
   "source": [
    "### empiric case -- standalone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ae5e643",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading input data k_core-gender-data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### PLOTTING THE RESULTS ####\n",
    "\n",
    "# setting common variables\n",
    "\n",
    "barwidth = 1.\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "fig.subplots(1, 1)\n",
    "\n",
    "### SUBPLOT 1 (GENDER FRACTION PER K-SHELL)\n",
    "\n",
    "ax1 = plt.subplot(111)\n",
    "\n",
    "\n",
    "# reading the data\n",
    "\n",
    "infilename = 'k_core-gender-data.dat'\n",
    "\n",
    "print('Reading input data %s ...\\n' %(infilename))\n",
    "\n",
    "myfmt = '%d %d %.8e %.8e %.8e'\n",
    "dummy = np.loadtxt(indirname+infilename, delimiter=' ')\n",
    "\n",
    "\n",
    "labelx = [repr(int(x)) for x in dummy[:,1]]\n",
    "valx = [int(x) for x in dummy[:,0]]\n",
    "valy_m = list(dummy[:,2])\n",
    "valy_f = list(dummy[:,3])\n",
    "valy_o = list(dummy[:,4])\n",
    "\n",
    "\n",
    "\n",
    "# plotting\n",
    "\n",
    "ax1.bar(valx, valy_o, width=barwidth, label='Unknown', color=mycolor_unkno)\n",
    "ax1.bar(valx, valy_m, width=barwidth, label='Men', color=mycolor_men,\\\n",
    "       bottom=valy_o)\n",
    "ax1.bar(valx, valy_f, width=barwidth, label='Women', color=mycolor_women,\\\n",
    "       bottom=np.add(valy_o, valy_m) )\n",
    "\n",
    "\n",
    "## setting axes features\n",
    "\n",
    "#ax1.set_ylim([-0.025,1.025])\n",
    "ax1.set_ylim([0.,1.0])\n",
    "ax1.set_xlim([-0.5*barwidth,len(valx)-(0.5*barwidth)])\n",
    "\n",
    "#xticksval = valx[::2]+[valx[-1]]\n",
    "#xtickslab = labelx[::2]+[labelx[-1]]\n",
    "\n",
    "xticksval = [valx[0],valx[-1]]\n",
    "xtickslab = [r'$\\;\\;\\;(k_s = '+labelx[0]+')$',r'$\\!\\!\\!\\!\\!(k_s = '+labelx[-1]+')$']\n",
    "\n",
    "ax1.set_xticks(xticksval)\n",
    "ax1.set_xticklabels(xtickslab)\n",
    "\n",
    "ax1.tick_params(axis='y', which='major', labelsize=25)\n",
    "ax1.tick_params(axis='x', which='major', labelsize=30)\n",
    "ax1.tick_params(axis='x', pad=40)\n",
    "ax1.tick_params(axis='y', pad=10)\n",
    "\n",
    "ax1.set_ylabel('Fraction of nodes', fontsize=28, labelpad=10)\n",
    "#ax1.set_xlabel(r'$k_s$', fontsize=30, labelpad=30)\n",
    "\n",
    "# textboxes\n",
    "\n",
    "ax1.text(0.05, -0.025, 'outer', transform=ax1.transAxes, fontsize=25, va='top', ha='center')\n",
    "ax1.text(0.95, -0.025, 'inner', transform=ax1.transAxes, fontsize=25, va='top', ha='center')\n",
    "\n",
    "ax1.annotate('', xy=(0.85, -0.05), xycoords='axes fraction', xytext=(0.15, -0.05),\\\n",
    "             arrowprops=dict(width=0.15, color='k', linewidth=3.5))\n",
    "\n",
    "\n",
    "\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "\n",
    "\n",
    "\n",
    "# legend\n",
    "\n",
    "#ax2.legend(bbox_to_anchor=(1.05, 0.6),fontsize=20)\n",
    "ax1.legend(loc=(0.03, 1.01),fontsize=22, ncol=3, frameon=False)\n",
    "\n",
    "\n",
    "fignameout = 'k_core-gender.pdf'\n",
    "\n",
    "plt.savefig(diroutname+fignameout, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c9379b0",
   "metadata": {},
   "source": [
    "### random case -- standalone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e1c08c88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading input data k_core-gender-rand_nrep-50-data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### PLOTTING THE RESULTS ####\n",
    "\n",
    "# setting common variables\n",
    "\n",
    "barwidth = 1.\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "fig.subplots(1, 1)\n",
    "\n",
    "### SUBPLOT 1 (GENDER FRACTION PER K-SHELL)\n",
    "\n",
    "ax1 = plt.subplot(111)\n",
    "\n",
    "\n",
    "# reading the data\n",
    "\n",
    "infilename = 'k_core-gender-rand_nrep-'+repr(nr_replicas)+'-data.dat'\n",
    "\n",
    "print('Reading input data %s ...\\n' %(infilename))\n",
    "\n",
    "dummy = np.loadtxt(indirname+infilename, delimiter=' ')\n",
    "\n",
    "\n",
    "labelx = [repr(int(x)) for x in dummy[:,1]]\n",
    "valx = [int(x) for x in dummy[:,0]]\n",
    "valy_m = list(dummy[:,2])\n",
    "valy_f = list(dummy[:,3])\n",
    "valy_o = list(dummy[:,4])\n",
    "\n",
    "# plotting\n",
    "\n",
    "ax1.bar(valx, valy_o, width=barwidth, label='Unknown', color=mycolor_unkno)\n",
    "ax1.bar(valx, valy_m, width=barwidth, label='Men', color=mycolor_men,\\\n",
    "       bottom=valy_o)\n",
    "ax1.bar(valx, valy_f, width=barwidth, label='Women', color=mycolor_women,\\\n",
    "       bottom=np.add(valy_o, valy_m) )\n",
    "\n",
    "\n",
    "## setting axes features\n",
    "\n",
    "#ax1.set_ylim([-0.025,1.025])\n",
    "ax1.set_ylim([0.,1.0])\n",
    "ax1.set_xlim([-0.5*barwidth,len(valx)-(0.5*barwidth)])\n",
    "\n",
    "#xticksval = valx[::2]+[valx[-1]]\n",
    "#xtickslab = labelx[::2]+[labelx[-1]]\n",
    "\n",
    "xticksval = [valx[0],valx[-1]]\n",
    "xtickslab = [r'$\\;\\;\\;(k_s = '+labelx[0]+')$',r'$\\!\\!\\!\\!\\!(k_s = '+labelx[-1]+')$']\n",
    "\n",
    "ax1.set_xticks(xticksval)\n",
    "ax1.set_xticklabels(xtickslab)\n",
    "\n",
    "ax1.tick_params(axis='y', which='major', labelsize=25)\n",
    "ax1.tick_params(axis='x', which='major', labelsize=30)\n",
    "ax1.tick_params(axis='x', pad=40)\n",
    "ax1.tick_params(axis='y', pad=10)\n",
    "\n",
    "ax1.set_ylabel('Fraction of authors', fontsize=28, labelpad=10)\n",
    "#ax1.set_xlabel(r'$k_s$', fontsize=30, labelpad=30)\n",
    "\n",
    "# textboxes\n",
    "\n",
    "ax1.text(0.05, -0.025, 'outer', transform=ax1.transAxes, fontsize=25, va='top', ha='center')\n",
    "ax1.text(0.95, -0.025, 'inner', transform=ax1.transAxes, fontsize=25, va='top', ha='center')\n",
    "\n",
    "ax1.annotate('', xy=(0.85, -0.05), xycoords='axes fraction', xytext=(0.15, -0.05),\\\n",
    "             arrowprops=dict(width=0.15, color='k', linewidth=3.5))\n",
    "\n",
    "\n",
    "\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "\n",
    "\n",
    "\n",
    "# legend\n",
    "\n",
    "#ax2.legend(bbox_to_anchor=(1.05, 0.6),fontsize=20)\n",
    "ax1.legend(loc=(0.03, 1.01),fontsize=22, ncol=3, frameon=False)\n",
    "\n",
    "fignameout = 'k_core-gender-rand.pdf'\n",
    "\n",
    "plt.savefig(diroutname+fignameout, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7474ddcd",
   "metadata": {},
   "source": [
    "### Both cases together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0158bd19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading input data k_core-gender-data.dat ...\n",
      "\n",
      "Reading input data k_core-gender-rand_nrep-50-data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### PLOTTING THE RESULTS ####\n",
    "\n",
    "# setting common variables\n",
    "\n",
    "barwidth = 1.\n",
    "\n",
    "fig = plt.figure(figsize=(25,12))\n",
    "fig.subplots(1, 2)\n",
    "\n",
    "### SUBPLOT 1 (EMPIRIC)\n",
    "\n",
    "ax1 = plt.subplot(121)\n",
    "\n",
    "# reading the data\n",
    "\n",
    "infilename = 'k_core-gender-data.dat'\n",
    "\n",
    "print('Reading input data %s ...\\n' %(infilename))\n",
    "\n",
    "dummy = np.loadtxt(indirname+infilename, delimiter=' ')\n",
    "\n",
    "\n",
    "labelx = [repr(int(x)) for x in dummy[:,1]]\n",
    "valx = [int(x) for x in dummy[:,0]]\n",
    "valy_m = list(dummy[:,2])\n",
    "valy_f = list(dummy[:,3])\n",
    "valy_o = list(dummy[:,4])\n",
    "\n",
    "\n",
    "\n",
    "# plotting\n",
    "\n",
    "ax1.bar(valx, valy_o, width=barwidth, label='Unknown', color=mycolor_unkno)\n",
    "ax1.bar(valx, valy_m, width=barwidth, label='Men', color=mycolor_men,\\\n",
    "       bottom=valy_o)\n",
    "ax1.bar(valx, valy_f, width=barwidth, label='Women', color=mycolor_women,\\\n",
    "       bottom=np.add(valy_o, valy_m) )\n",
    "\n",
    "\n",
    "## setting axes features\n",
    "\n",
    "#ax1.set_ylim([-0.025,1.025])\n",
    "ax1.set_ylim([0.,1.0])\n",
    "ax1.set_xlim([-0.5*barwidth,len(valx)-(0.5*barwidth)])\n",
    "\n",
    "xticksval = [valx[0],valx[-1]]\n",
    "xtickslab = [r'$\\;\\;\\,(k_s = '+labelx[0]+')$',r'$\\!\\!\\!\\!\\!(k_s = '+labelx[-1]+')$']\n",
    "\n",
    "ax1.set_xticks(xticksval)\n",
    "ax1.set_xticklabels(xtickslab)\n",
    "\n",
    "ax1.tick_params(axis='y', which='major', labelsize=30)\n",
    "ax1.tick_params(axis='x', which='major', labelsize=35)\n",
    "ax1.tick_params(axis='x', pad=40)\n",
    "ax1.tick_params(axis='y', pad=10)\n",
    "\n",
    "ax1.set_ylabel('Fraction of nodes', fontsize=38, labelpad=10)\n",
    "#ax1.set_xlabel(r'$k_s$', fontsize=30, labelpad=30)\n",
    "\n",
    "# textboxes\n",
    "\n",
    "ax1.text(0.05, -0.025, 'outer', transform=ax1.transAxes, fontsize=30, va='top', ha='center')\n",
    "ax1.text(0.95, -0.025, 'inner', transform=ax1.transAxes, fontsize=30, va='top', ha='center')\n",
    "\n",
    "ax1.annotate('', xy=(0.85, -0.05), xycoords='axes fraction', xytext=(0.15, -0.05),\\\n",
    "             arrowprops=dict(width=0.25, color='k', linewidth=5.5))\n",
    "\n",
    "\n",
    "ax1.set_title('Empirical', fontsize=42, pad=20, color='dimgrey')\n",
    "\n",
    "\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "\n",
    "\n",
    "\n",
    "### SUBPLOT 2 (RANDOM)\n",
    "\n",
    "ax2 = plt.subplot(122)\n",
    "\n",
    "# reading the data\n",
    "\n",
    "infilename = 'k_core-gender-rand_nrep-'+repr(nr_replicas)+'-data.dat'\n",
    "\n",
    "print('Reading input data %s ...\\n' %(infilename))\n",
    "\n",
    "dummy = np.loadtxt(indirname+infilename, delimiter=' ')\n",
    "\n",
    "\n",
    "rand_labelx = [repr(int(x)) for x in dummy[:,1]]\n",
    "rand_valx = [int(x) for x in dummy[:,0]]\n",
    "rand_valy_m = list(dummy[:,2])\n",
    "rand_valy_f = list(dummy[:,3])\n",
    "rand_valy_o = list(dummy[:,4])\n",
    "\n",
    "# plotting\n",
    "\n",
    "ax2.bar(rand_valx, rand_valy_o, width=barwidth, label='Unknown', color=mycolor_unkno)\n",
    "ax2.bar(rand_valx, rand_valy_m, width=barwidth, label='Men', color=mycolor_men,\\\n",
    "       bottom=rand_valy_o)\n",
    "ax2.bar(rand_valx, rand_valy_f, width=barwidth, label='Women', color=mycolor_women,\\\n",
    "       bottom=np.add(rand_valy_o, rand_valy_m) )\n",
    "\n",
    "\n",
    "## setting axes features\n",
    "\n",
    "#ax1.set_ylim([-0.025,1.025])\n",
    "ax2.set_ylim([0.,1.0])\n",
    "ax2.set_xlim([-0.5*barwidth,len(rand_valx)-(0.5*barwidth)])\n",
    "\n",
    "xticksval = [rand_valx[0],rand_valx[-1]]\n",
    "xtickslab = [r'$\\;\\;\\,(k_s = '+rand_labelx[0]+')$',\\\n",
    "             r'$\\!\\!\\!\\!\\!(k_s = '+rand_labelx[-1]+')$']\n",
    "\n",
    "ax2.set_xticks(xticksval)\n",
    "ax2.set_xticklabels(xtickslab)\n",
    "\n",
    "ax2.tick_params(axis='y', which='major', labelsize=30)\n",
    "ax2.tick_params(axis='x', which='major', labelsize=35)\n",
    "ax2.tick_params(axis='x', pad=40)\n",
    "ax2.tick_params(axis='y', pad=10)\n",
    "\n",
    "#ax2.set_ylabel('Fraction of nodes', fontsize=28, labelpad=10)\n",
    "#ax1.set_xlabel(r'$k_s$', fontsize=30, labelpad=30)\n",
    "\n",
    "# textboxes\n",
    "\n",
    "ax2.text(0.05, -0.025, 'outer', transform=ax2.transAxes, fontsize=30, va='top', ha='center')\n",
    "ax2.text(0.95, -0.025, 'inner', transform=ax2.transAxes, fontsize=30, va='top', ha='center')\n",
    "\n",
    "ax2.annotate('', xy=(0.85, -0.05), xycoords='axes fraction', xytext=(0.15, -0.05),\\\n",
    "             arrowprops=dict(width=0.25, color='k', linewidth=5.5))\n",
    "\n",
    "\n",
    "\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "\n",
    "\n",
    "ax2.set_title('Random', fontsize=42, pad=20, color='dimgrey')\n",
    "\n",
    "\n",
    "\n",
    "# legend\n",
    "\n",
    "#ax2.legend(bbox_to_anchor=(1.05, 0.6),fontsize=20)\n",
    "ax2.legend(loc=(1.05, 0.4), fontsize=32, frameon=False)\n",
    "\n",
    "fignameout = 'k_core-gender-comparison-empiriv_vs_random.pdf'\n",
    "\n",
    "plt.savefig(diroutname+fignameout, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "820ce8db",
   "metadata": {},
   "source": [
    "# Multilayer (multiplex) network viz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59254282",
   "metadata": {},
   "source": [
    "## Visualization of Jaccard matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "26570c44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading input data graphs-edge-jaccard_data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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EhErxzsPuvEOHOHToEBKhX9afvl9G3foNOa1efcqXL0+vywcwf/aMIjKfzp7JFVf9BYCL+/Tjm6+/QFWZP3sGvS4fQIUKFahTtx516zfkp++j5yVbtnQJDRo2pH4D573tf+XVzJj+cRGZmdOnMfAv1wFw+RUD+OLzz1BVZkz/mP5XXk2FChWoV78+DRo2ZNnSJQCc07UbycneX0gjTIwE1wyfRNIAn+b+3Oj9GiIS59VK7TtJSo1Etvyxu/A6fftuUmokklI9kS3bvfqrJ5JSI5H07Vke/Vmk1EiMjC5VK7Hlz5wja2fmkFq1UlGZKkdk8g8r2XsPUrXyKQB0bFKD7/59Fcteuoq7Xvmq0CBHg21bM0hJPfK3tXZKKtu2pheVycggJdVx6cTFxZGQkMCunZls25pe2O/MTWPbVidMMz8/n4u6dqR1kzS6nXsB7Tp0ioy+2zKonZJaeF0rJZU/tmUUkfljawa1UlML9Y2v7Oj7x7YManvoW6t2CtvcuYJw/dV9uPyiLrz31riI6Lo1I53UtCPvbWpqKlszir63GRkZpLkycXFxJCYkkpmZydaMdNLSjuiakpp21NyoEsj/W9AMn4QVBSEiBfPrAv8BfuToUya+Tp08DowI57UNWLpuO+3/NommaUm8Pux85nz3O7mH8gNPPI6IjY1l3oKlZGXt5sa/XMXPq1fRrEXLwBNLiYnTP6VW7VQyd2xn6FV9aNC4KZ3OOi5TzR4bLAwtLMJ5Z6rinDw5BKwH2gJXqKr3d9+7gY5ezb+TNspkbM8irWZS4XVqjSQytmeRsSOLtBpe/TuyyNieRarHjje1RiIZHjvisHTJ3EtatcJ4c1KrxpOeWdR9krHziExsjJBQqTyZew4UkVm7ZTc5Bw7Rsm70vm7Wqp1CRvrvhddbM9KpVTu1qExKChnpWwDIy8sjOzub5CpVqVU7tbDfmbuFWrVTisxNTEzi7K7d+WL+HCJBrVopRXaC2zLSqVmr6GvWrJ3CtvT0Qn1z9jj61qyVwlYPfbdtzaCWO7fgnqtWr8FFl/bhpx/Cd03UTkklfcuR9zY9Pb3I7h0gJSWFLa5MXl4eWdlZVK1aldopqWzZckTXjPQtR82NKqHngjAIPwqiI9AZuAUnBdwEH+6F9aq6zKtleC92rJi5YBUDL3UiHDq1qkt2zn62ZWYzb/FaLuzclKTKFUmqXJELOzdl3uK1bMvMZs/eA3RqVReAgZd2ZMZXKyOiy7JfttOodiJ1a1SmXFwMV3ZtyMwlm4rqu2QTg85vAsAVZzfgy5+ct65ujcqFD91Oqx5P07QkNv/hK5FUZDijXQc2/rqe3zZv5ODBg0ybOokel/QuItPj4t58MNE5yj9z2lTO7nYuIkKPS3ozbeokcnNz+W3zRjb+up627TuS+ecOsrJ2A7B//36++nw+DRs3jYi+p7dtz6YN6/l98yYOHjzIzI8mc0HPXkVkLuh5KVMnvQPA7Okf0vmc7ogIF/TsxcyPJpObm8vvmzexacN6WrfrwL69e8nJcd7jfXv38vUX82nSrEXYurbv0JFf169n00bnvZ3ywfv06t2niMylvS9jwjtvAfDR1Ml0P/c8RIRevfsw5YP3yc3NZdPGjfy6fj0dOkbGjRMUEhNEGJrtgP0RjgsiT1UL/vx/KyL7cZJmXImTc7NUeHPkYLq2b0S1pEqsn/4YI/83m3Jxjg/q9amLmL1wNT27NGfV1OHsO3CQW0a+B8Cu7H08OW4uX4+/G4BRr89lV/Y+AIY9PYWx/7iWihXKMXfRGuYsikwQR/5h5e6xXzN9RC9iY4Q3569lze+7eHRgB75fv4OZSzYzft7PvHH3+ax89Vp27cll8LNOSFSXFrW4r39bDuUd5rAqw15dULgzfvPeC+jaKoVqCaewftxfGDlxGW9++nNYusbFxfHPp19gYP/eHM7P5+pBQ2navAXPjHqcNme0o8elfbhm8PXcdev1nN2uOUnJVfjvOMcYN23egj6XD+C8zm2IjYvjX8+8SGxsLH9s28bfb7+Rw/n5HD58mD79BnDRxb0CaBK8vo89+Rw3XHMZ+fn5DLj2Oho3a8ELo5/g9DbtuODi3lw5cCj33XkjF5zZiqSkZJ5/zTFwjZu14JLLruCSru2Ii4tjxFPPExsby587tnPH9dcAkJefR59+V9Ht/B4R0fXZF17i8j6XcDg/n8FDrqd5i5b88/HHaNu+Pb16X8Z1Q2/grzdcR5sWTUiuUoX/e2sCAM1btOSK/lfS8YxWxMbFMebFfxMb63zerx88kAULviTzzz9p2vA0Hn7kMYZcf2PY+hbBXBBhIV4pNYObJDICuFNVq3n0CU4ijIOq2k5E6uE8kOujqjN8LuTMG49TlqRDEC+tYGXpI4mVpY8eZbQsfUn8BSkNBo1N37rTf9QRQO0qldjw7s2pQKl98z1eidhRZFVVERkFvCsiFwC/ukNN3XIgnhxQ1R89rpNFxFeS5Fmqui9SOhqGEWFsBxwWkc4F8T5OdMMDOH5hgKOPIDnGuZHHdQPgAx9y9YFNkVPPMIyIEkyYmYWh+SUkA6yqI/ARRqaq+ThHkgsI+HVGVYcCQ0PRwzCMUqbgJFwgGcMnlg3NMIzQsWxoYWEG2DCM0DEfcFiYATYMI3QkCB+wpaP0ixlgwzBCx1wQYWHfDQzDCBnH/kqA5m+uVBGRD0Vkr4hsFpGBfuREREaLSKbbRrvnDhCRaiKy0O3fLSLfuBWWPeff7RaNyBaRN0SkQqTfh1AxA2wYRugENL7F7pBfBg7ilJEfBLwiIr4yMd2MU7yzDdAa6MORMNcc4AagOpAMjAamFyQKE5GewEM41ZHr4oS8Ph7ubUcKM8CGYYSMxEhQ7ah5IpWA/sCjqpqjql8DHwODfbzMEGCMqm5R1XRgDG7oqqoeUNW1qnoYJ+w1H8cQV/GYO05VV6nqLmAkx1HYq/mADcMIhZuBOz78fxdxKL/4I9blYmPA2eF61rFqgpNPZp1H33Kgu48lWnKk4EOBXJGdsoj8BDQDygGvq+p2j7nTvObWFJGqqppZrOLHADPAhmGEwlhgRv/R89O37So+J0et5Iqse+XKd72644Fsr74soLKPJeLdMU+5eBERdZPZqGprETkF6IeTmbG4ubivYwbYMIyyS6GfN4CMD3KABK++BMBXTlVv2QQgR70yianqAWCiiKwRkR9Vdbmfufh5nWOO+YANwwgZkSB8wL4N8DogTkQae/S1AVb5kF3ljgWSK6AczsM2f3P/OB7cD2AG2DCMMAgcgubbAKvqXmAq8ISIVHJDx/oCb/t4mbeAe0QkVURSgHuB8e7rdxaRc0SkvIhUFJEHcaIqvvWYe6OItBCRJOCRgrnHA2XSBeGdb/d4xzvn7vGMd87d4xnvfLvHOx45d08YwnBBANwOvAFsx/HH3qaqq0SkK/CJqhbU63oNZ0e7wr1+3e0DqAC85I4fcmV6FVTdUdXZIvI08DlQEZgCPFbC24waISVkL0XKlLKGUQYpUUL2lnd9lL5td4CHcEkVWfXS5ZaQ3QdlcgdsGMbxgZOLJ8AO+MTb+EcMM8CGYYROEC4IywXhHzPAx4DjuYadtz/9QF4pKRIEp3h9Wp/9ckPpKBIk93VvUOS6LL23wRKmD/ikxwywYRghUxCGFkjG8I0ZYMMwQsZ2wOFhBtgwjJAxAxweZoANwwgZM8DhYQbYMIyQ8Zdu0lvG8I0ZYMMwQsZ2wOFhBtgwjJAxAxweZoANwwgdIfDhZbO/fjEDbBhGyIgIMTHFnzW2HbB/zAAbhhEy5oIIDzPAhmGEjBng8DADbBhG6JgPOCzMABuGETIxMTEBfcCBxk9mzAAbhhEyIoGzTZoHwj8l+tMkIv1F5DMR2S0iuSKyTkSec+s0FcicJiLjRCTdldkkIi+KSDWvteqJiHq0HBFZLiI3RermDMOILqHWhDMcgjbAIjIGmARsAAYDPYDngQuAl12ZlsB3QGdguCvzJNAP+NbTUHtwH3CWK7Mc+J+I/CXE+ylTvPrINWye/QTLJj7gV2bMvf1YOeVhlrx7P2c0TSvsH9SrIysmP8yKyQ8zqFfHwv62zdJYOuF+Vk55mDH39ouovnPnzKZ1y6a0bNaIZ55+6qjx3Nxc/jLwalo2a0TXLmeyedOmwrFnRj9Jy2aNaN2yKfPmzgl6zXD4+dsveXrwhTw18Dw+e/fVo8bzDubyzuN/46mB5/HSbVewc+sWAPZm7eLVvw9k+MWn8+ELI4rMeWXYQJ4efCHP3dib527sTc6uPyOia1l7bwso2AEHaoZvgjLAItIHuAf4q6repKrTVfVLVX0FaAeMFefP3DvALuAsVR3vyryGY2CTgP/6WH6tqi5W1XnAEGANcF3Yd1YGeHvmEvoOG+t3vGeX5jSsU51W/Udx55OTeOnBAQAkJ5zK8Jt60u2GF+h6/fMMv6knSZWdApUvPTiAO0ZNolX/UTSsU50eZzWLiK75+fn8/a47mDb9E374aTUfvDeRNatXF5EZ/8Y4kpOSWfXzev427G6GP/wgAGtWr+aD99/j++Wr+HjGbIb97Xby8/ODWjNUDufn8+GLI7hx9Bvc9+YcfvxsOn9s+qWIzJJZH1AxPpGHJnxOtwHXM2vsaADKla9Azxvuofdt/8/n2tcOf457xs3gnnEziE+u5lOmJJS199aTGBFiYgI0PxZYRKqIyIcisldENovIQD9yIiKjRSTTbaNde4OINBGRaSKyQ0R2isgcEWnqMXeoiOS737AL2rkRfyNCJNgd8N3A96r6hveAquar6idAN+AM4J+qmu0lk45TufQyEann70XUqRC6AqgTpF5lmoU/bGBn9l6/4727tWLCrKUALFm5mcTKFalVNYGLOjdl/rdr2ZW9j9179jP/27X0OKsZtaomULnSKSxZuRmACbOW0qf76RHRdemSJTRs2Ij6DRpQvnx5rrz6GmZMn1ZEZsb0aQwaPASAK/oP4IvP5qOqzJg+jSuvvoYKFSpQr359GjZsxNIlS4JaM1R++3k51VLrUjXlNOLKleeM83uzauGnRWRWLfyU9hdfAcDp3S/hl+++QVUpX/FU6rfuQFz5Y1Mhuqy9t55IIONbfLKel4GDOGXkBwGvuN+ivbkZuBxoA7QG+gC3uGNJwMdAU3edJYD3jX6jqvEe7YtQ7zfSBDTAIlIO6ALMDiDazf35kZ/xj3ACUs4JsM5pwMZAep0MpNRIZMsfuwuv07fvJqVGIinVE9my3au/eiIpNRJJ357l0Z9FSo3EiOiSkZFOWtqRv4upqWmkp6cfLVPHkYmLiyMhMZHMzEzS04+em5GRHtSaoZK94w+SqtcuvE6sXousHX8Ukcnasa1QJjYujlPiK7Mva1fAtSeNfpDnbuzNvLf+TSSqipe199aTUF0QIlIJ6A88qqo5qvo1jiEd7ONlhgBjVHWLu5kbAwwFUNUlqjpOVXeq6iEct2hTEaka8ZuNAsFEQVQFKgC/BZBLBXZ773492Owh50mMiMQBlXHe6HbARUHoZRjHnIGPPEdi9Voc2JfDW/+4g+/mfkiHnleUtlqlwc3AHa8NaUfe4eL/CMU5O+BBwDMe3U2APFVd59G3HOjuY4mW7pinnK+dMjgbwW2qmunR11ZE/gR2Am8DT6rqcVGhryRhaOH/qfeN99eFYar6VZReq0yRsT2LtJpJhdepNZLI2J5Fxo4surZrVKR/wffrydieRarHjje1RiIZHjvicEhJSWXLlt8Lr9PTt5Camnq0zO+/k5aWRl5eHtlZWVStWpXU1KPnpqQ4cwOtGSoJ1Wuye8fWwuusHdtIrF6ziExi9Vrs3rGVpBq1yc/L40DOHk5NTC523cTqtQA45dR42l7Qh9/X/BS2AS5r763LWGDG7e/8kL5jz8FiBatXLs/8+7u969UdD3hv1rJwNmLexLtjnnLxIiLq8RVERNJw3Br3eMh+BbTC2QC2BN4H8nCCA0qdYHzAmUAujmugONKBJBFJ8DNe10POk7uBjkAvYBHwrIi0CUKvE56ZC1Yx8FInwqFTq7pk5+xnW2Y28xav5cLOTUmqXJGkyhW5sHNT5i1ey7bMbPbsPUCnVs5bPfDSjsz4amVEdOnQsSPr1//Cpo0bOXjwIB+8/x69el9WRKZX78t49+03AZg6ZTLdzzsfEaFX78v44P33yM3NZdPGjaxf/wsdO3UKas1QqdO0NX9u2cTOrb+Td+ggP342gxZdLigi06LLBXw3eyoAK778hEbtzio2ZCo/L4+9u3e6/z7Emm8+p1b9JmHrWtbeW0/CCEPLAbxtRQKwJwjZBCDHy/hWB+YC/1XViQX9qrpBVTeq6mFVXQE8AQwI8t52isi1wciGSsAdsKoeEpGFQE/gkWJEC3atl+FEQ3hzGc4ueoFX/3pVXQYgIt8AvwBPAZcE0q2s8+bIwXRt34hqSZVYP/0xRv5vNuXiYgF4feoiZi9cTc8uzVk1dTj7DhzklpHvAbArex9PjpvL1+PvBmDU63PZlb0PgGFPT2HsP66lYoVyzF20hjmL1kRE17i4OJ5/8T/06dWT/Px8hgy9gRYtW/LEiH/Qrn0Heve5jKE33MgNQwfTslkjkpOr8Pa7jr4tWrak/5VX0bZ1C+Li4njhpZeJjXXu09eakSA2Lo7Lhz3G/+4fyuHDh+l0yQBq1W/CnDeeJ63p6bQ8+0I6XXoV7426l6cGnsepCUkM+seLhfNHXd2NA/tyyD90iFVfz+Ovz44nuWYq/3tgKPl5eejhwzRu34Uze18dtq5l7b31JIyDGOuAOBFprKoF4SltgFU+ZFe5Y0t8yYlIMo7x/VhV/xVAZSX4w9GHgQ7AxECCoSLBPERww9A+Boaq6pteYzE48b5zgO+BU4H2qprjIVMbJ7phgar2c/vq4Txs66OqMzxk7weeBtqo6k9eqkTLDRJVKra9s7RV8Mv+H/5T5PrAceEZ880pXtuFZ7/cUDqKBMl93RsUuS4j721JonZTejz3dfqfOcW7IKrFl2fuPeekAhme/SLyHs7v9E04EVSzgC6quspL7lZgGHChKz8P+Leqvup+4/4UWKKqR/2iicglOBFcf4hIM2Ay8IGqPh7o5kRkAs5ueQzwDY7LpEQ2SFW/LG48KB+wqk4XkeeAcSJyNo7fNgdoBtwKbFLV2SIyGPgcWCwiTwObXJnhOH6bO4J4uVeAh4D78f1E1DCM44Qws6HdDrwBbMdxdd6mqqtEpCvwiarGu3KvAQ1wNnEAr7t94Bzg6gi0FJGhHmu3UNXfcA6KjReReOAPnG/no4K8vXuBxsADhJ5SqFg3b9AP4VT1XhFZBNwJTAAq4hjYj4FnXZmVItIeeAzHjVAN2IoTgjZSVQMeG1LVHBF5EXhURB5W1d8DzTEMo3QIJxeEqu7Eie/17l+A8+Ct4FpxjOBRR0bdb+Rvevd7jN+Hc9q2xKjqVqCjiLQDTseJOY4NZS1/lCgZj6pOAaYEkPkNuDGItTbh56+Kqj6B4yw3DOM4JswdcJlAVb/Hca9GHMuGZhhGyBScdgskY/jGDLBhGCFj6SjDwzIlG4YRMidTOko3KdCDIrJURL4XkXs8xuJEpKn7sC9ozAAbhhEyJ1k6ygdwIiiSgUScQ2O3u2OKEwH2UEkWNANsGEbISBDpKE+UHTBOcMGnQGNVbYiToOwWcLJC4gQoXFiSBc0AG4YRMieTCwInTe5UjyPQk3HijwtC09YBDUuyoD2EMwwjZE6GMDQPtuG4HgrIwNnEVgZ24yT58ZVMyC+2AzYMI2ROMh/we8C1bvpccJLJw5HDGd2BEh0cMwNsGEbIxMQQuCTRiWNlxuMY3U9FpCdHcpu3EpEngCtx3BJBYy4IwzBC5iRzQazGOb2rHKkAVBD9oDgZ2Up0gtcMsGEYIXOSHcQYwdHpEw7jFCL+tiCtbkkwA2wYRsjEiP+qx54yJwKqOjLSa5oBPgZ459w9nvHOuXs8451v93inLL23wVIQBxxIxvDNieMeNwzjmBMjwbUTBREpJyL3icgPIrLXbT+KyANuBfkScQL+TTYM41hxMj2EE5HyONU4uuJUWP7BHWqGW0ZNRHqo6qFg17QdsGEYIXOSxQHfC5wDjARqq+o5qnoOkAL8Cycy4u6SLGg7YKMIZal+3U2TIlPxOVq8flWrIte79uWXkiaBST41tEIPsSLEBrCwgcbLEAOBuar6mGenqh7EqeDTAbgWp6ZlUNgO2DCMkDnJckE0xIn19cc8oElJFjQDbBhG6ATjfvBjf0Wkioh86D7I2iwiA/3IiYiMFpFMt40W16qLSBMRmSYiO0Rkp4jMEZGmXvPvFpFtIpItIm+ISIUQ7/aA24obD9r/C2aADcMIg1gRYmMCNP874JdxjvbWBAYBr4hISx9yN+MU72wDtAb64KaBxCmU+THQ1F1nCU7VdgDcI8MP4VRHrotTXTlgSXo//AL40q+A012ZoDEDbBhGyITqghCRSkB/4FFVzVHVr3EM6WAfLzMEGKOqW1Q1HRgDDAVQ1SWqOk5Vd7rRB88DTUWkqsfccaq6SlV34TxAGxri7b5B8S6Gxjj5IoLGHsIZhhEKNwN3DO/RkPzDWqxgrBMIPAh4xqO7CZCnqus8+pbjZBTzpqU75innbyfaDdimqpkec6d5jC8HaopIVQ+ZoFDV14DXihkvUTJ2MANsGEZojAVmPPXpr+m79ucVK5hcMY5Xrzr9Xa/ueCDbqy8L3/l0490xT7l4ERGP5OiISBqOW+OeAHNxX6dEBjgamAvCMIyQKcgFEaj5IAdI8OpLAPYEIZsA5HgZ3+o4EQr/VdWJAebi53UCEumTcGaADcMIGQniGLKfZ3DrgDgRaezR1wZY5UN2lTvmU05EknGM78eq+q8g5v5RUveD+zrlcWrCPY1TnugHt6XhnISbW1IjbAbYMIyQCfUhnKruBaYCT4hIJRE5G+gLvO3jZd4C7hGRVBFJwTmRNt59/QRgDrBQVX1VJH4LuFFEWohIEvAIJXxQ5kHET8KZATYMI2TCPIp8O1AR2A5MBG5T1VUi0lVEcjzkXgOmAyuAlcBMjjwM6wd0BK4XkRyPdhqAqs7G2bF+DvwGbAaKnGQrAYUn4TzzPajqQVV9FGcXfm1JFrSHcIZhhExBrG8gGV+o6k6c+F7v/gU4D88KrhV4wG3esm8Cbxb3+qr6HPBcsUoGR0OcUDR/zMPZHQeNGWDDMEJGCCIbmr+jcGWPiJ+EMwNsGEbIFHPSuIjMCULZOgknIv1F5DMR2S0iuSKyTkSecx3piIh6tMMikiEi74tI/WjqZRhGZJAgQtBOoGQ8ET8JFzUDLCJjgEnABpzjhT1wjglegBMsXcAY4CzgbOA+oB0wU0Rsd36c8eoj17B59hMsm3iUK66QMff2Y+WUh1ny7v2c0TStsH9Qr46smPwwKyY/zKBeHQv72zZLY+mE+1k55WHG3Nsvovq2rBXPPy9uzKhLGnNJs2pHjcfFCLd0rsOoSxrz8AUNqHqqE0FUqXws951bj//0a87AtrWLzOlUJ5ERPRoxokcj/t61LvHlQ0vj6M2nc2fT6YwWtD+9KS88O/qo8dzcXG647lran96UC7ufxW+bNxWOPf/MU7Q/vSmdzmjB/HlzANiy5Xcuu+QCOrc/nbM6tObVl1+KiJ7eBC5JH7hkUVlBVV9T1R7FjF+oqi/7G/dFVAywiPTBOY3yV1W9SVWnq+qXqvoKjoEd6yG+SVUXq+o3qjoBGAY0p4Rp3Yzo8/bMJfQdNtbveM8uzWlYpzqt+o/izicn8dKDAwBITjiV4Tf1pNsNL9D1+ucZflNPkipXBOClBwdwx6hJtOo/ioZ1qtPjrGYR0VUEBrVL4YUFm3h0zno6nZZI7YSiSbDOqZ/M3kP5PPzJL8xbl8mA1rUAOJR/mI9WbueDn7YVkY8RuKZtbZ79YiMj5q5nS9YBzm9clXDJz8/ngXvuYtKHM/jmuxVM+eB9fl6zuojMO2++QVJSMt+tWMttd/6dEY/+PwB+XrOaqZMnsWjZT3zw0Uzuv/tv5OfnExcbx8hRz7D4uxXM/Xwh48a+ctSakeAkS8gecaK1A74b+F5Vj3piqKr5qvpJMXMLTqiU+FSJEV0W/rCBndl7/Y737taKCbOWArBk5WYSK1ekVtUELurclPnfrmVX9j5279nP/G/X0uOsZtSqmkDlSqewZOVmACbMWkqf7qdHRNf6VSqyPSeXP/ceIv+wsuS3LM5IKXrK9YzUyizatAuA77Zk0axmJQAO5ivr/9zHofyiOQ4K/J3l45xfm1PKxbJ7f4meufjku2VLqN+gIfXqN6B8+fJcMeAqPpnxcRGZWTM+5ppBTp6avv3689UXn6GqfDLjY64YcBUVKlSgbr361G/QkO+WLaFW7dq0adsOgMqVK9OkaTO2ZqSHras3YZyEK3O4aTEfFJGlIvK9iNzjMRYnIk1FJL64NbyJuAF2T4J0AWYHq4OrfDkRaYKTKu4XnHg/owyRUiORLX/sLrxO376blBqJpFRPZMt2r/7qiaTUSCR9e5ZHfxYpNRIjoktyxXLs2nfEOO7an0dyxXJ+ZQ4r7D90uFiXQr7CO99n8HjPRjzbpykpCRVYsHFX2LpuzcggNa1O4XVKahpbt2b4lYmLiyMhIZGdmZls3epjbkbRub9t3sRPy3+kfcczw9bVm5NsB/wAMApIBhKBZ0XkdndMcWKNfR0G8Us0dsBVgQo4Qc/B8CJO6MZBYC2OI7u/qh6/9VuMk5JYgXMbVuGJub9y3/S1bNl9gEubVS9ttYolJyeHIQOvYtTTz5GQ4J16IXxi3JJExbUTZQcM3IhzFLmxqjbE2WTeAs43e2AKUKKMaNGMgig+R90RnsE5ydIR6IWTLm6WiKRGSzEjOmRszyKtZlLhdWqNJDK2Z5GxI4u0Gl79O7LI2J5FqseON7VGIhkeO+Jw2LX/EMmnHtnxJleMY5eXu8BTJkagYrkYcg76/7tfJ8nxW+/YexCApb9n0ajaqWHrWjslhfQtvxdeZ6RvoXbtFL8yeXl5ZGdnUaVqVWrX9jE3xZl76NAhhgy8kgFXX0ufvpF9wFlAqEeRyyh1gKkeSYAmAy1FpOBr0zqcwxpBEw0DnAnkAqcFKf+bqi5z2yycJM2nUMIz1UbpM3PBKgZe6kQ4dGpVl+yc/WzLzGbe4rVc2LkpSZUrklS5Ihd2bsq8xWvZlpnNnr0H6NSqLgADL+3IjK8i43natHM/NeMrUK1SOWJjhE6nJbI8o2gCrOUZe+hSLxmA9mmJ/Lzdv38bHINdO6EC8RWc37cWteLZmp0btq7t2ndkw6/r2bxpIwcPHmTq5Elc3KtPEZlLevXhvXedNAnTPpxC1+7nISJc3KsPUydPIjc3l82bNrLh1/W079AJVeWu2/5Kk6bNueOu6P0qBUrEU9BOELbhuB4KyMCxoQUPF/LwnU7TLxEP9VLVQyKyEOiJk/iipPNzRWQDTiSEcRzx5sjBdG3fiGpJlVg//TFG/m825eIcY/T61EXMXrianl2as2rqcPYdOMgtI98DYFf2Pp4cN5evxzuGYNTrc9mVvQ+AYU9PYew/rqVihXLMXbSGOYvWRETXwwoTvs/g793qESPCwo27yMjOpW/LGmzatZ/lGXtYsGEXN52ZxqhLGrP3YD6vLT6yk3yqVxMqxsUQGyOckZrA819tYmt2LtNXb+fB8+qTfxgy9x3kjSXhP9iKi4vj6TEvMqDvpeTn5zPouqE0b9GSUSMfo227DlzSqw9/GXIDt940hPanNyU5OZnX35wAQPMWLbm8/wDOan+6s85zLxEbG8viRV/z/sR3aNHydLp1bg/AoyNGctHFl4atryex4v+osafMCcJ7wLUi8pyq5uG4TQEKdsDdgd99zvSDeKTUjBhuGNrHwFD3rLbnWAzQQ1Vni4gCf1PV/3iMnwJsASar6q1eS0deWaMIVpY+cpTRsvQlMZcpI+etT8/OLf6+EirE8uhFjVJxdoxlFrfY51vAfuBJoAZOHorzcM43DAeeVtX/F+yaUTnsoKrTReQ5YJybZm4aTmLkZsCtwCaOREnUE5HO7r+rA3fgbPPHRUM3wzAiRzBRDieOC5jVOH+gFCf1JByJflCcbGhPlGTBqJ02U9V7RWQRcCcwASft3CacnfGzHqL3ug0c//EKnB3y0mjpZhhGZJAgohxOoIdwIzj6G8JhYBfwraouK+mCUT3uq6pTcEIz/I2fMP9lDONkJFYC+3hPFB+wqpYo1WQwWL4FwzBCJpiTbidQHHDEMQNsGEbInEw+YBEZEoycd+BBcZgBNgwjZIKJ8z2B4oDf4MhDOE+879AMsGEY0SdGApckOoFcEOf56DsVJ33C7TgVMe4ryYJWlNMwjJAJ5ySciFQRkQ9FZK+IbBaRgX7kRERGi0im20aLR2iFiIwVkbVuUYehXnOHiki+V8HOc0O5V1X9ykebrar/Bs7A2dB2KcmaZoANwwgZCfJ/fngZ5zRZTWAQ8IqI+Cr5czNO8c42QGugD24SHJflODvQ7/28zjeqGu/RvijpfQZCVXOBt4Gg/MQFmAE2DCNkQt0Bi0glnLwvj6pqjqp+jXNGYLCPlxkCjFHVLaqajlNFZ2jBoKq+rKrzKb5g5rEgGzjNPe0bFOYDNgwjFG4G7rjqjNocDpAgwDXAg3AyHxbQBMhT1XUefctx8il409Id85QrrjimN21F5E9gJ84u9Uk3l0Ok+QG4CScd7/5gJpgBNgwjFMYCM6au2Ja+t5gUnuDU2Lv1rLrvenXH4+wYPcnCdzaxeHfMUy5eRMQjNaQ/vgJaAZtxjPb7OFnLngwwzycikoSTmL0v0MDt3oCTbmG0qn5bkvXMBWEYRsiEUREjB/DOEJ/AkZJkxckmADlBGF9UdYOqblTVw6q6AidXw4Bg7s0bEUkDfgQexKmKscRtyW7fj65M0JgBNgwjZMKoCbcOiBORxh59bYBVPmRXuWOB5IJBKVnGN0+eA1KAG4BUVe2uqt2BVOB69+dzJVnQDLBhGCETGxNc80ZV9wJTgSdEpJKbNbEvjo/Wm7eAe0QkVURScJJ3jS8YFJHybhpbAcqJyCkFD8JE5BIRqen+uxnwKI67IBR6AK+p6pueu291eAt4xZUJGvMBG0Xwzrl7POOdb/d4xyPn7gmDIMQE2FAWE4Z2O87psu04mRBvU9VVItIV+ERVCyoMv4bjb13hXr/u9hUwlyMP77rg+KfPA77AydM73q1W/AfwDk5hzVApbue9hhLurqOSkD2KlCllDaMMUqKE7OOX/p6+71DxD+FOLRfL0I51ToSE7B8BB1T1Gj/jE4FTVbVvsGuaC8IwjJA5yWrC3Q10FpEXRKRWQaeI1BKR53F23yUqwGcuCMMwQiY2JnAuiEDjZYj5OLkf7gLuEpHdbn+S+/NPYL5XAnpR1Xr+FjQDbJRZjuf6dXC0P33vwePXg1apfGhG8iTLB7yR0CMofGIG2DCM0AkiH3BkTVbpoaoXRHpNM8CGYYSMU5LopClLH3HMABuGETJC4A2u2V//mAE2DCNkTjIfcMQxA2wYRsjYDjg8zAAbhhEyMSLEnDwliSKOGWDDMEJGCHyay8yvf8wAG4YRMiKCBNjhBho/mbGjyIZhhIwE2U4ERKS5iEwRkWGRWtMMsGEYIRMrElQ7QeiNU4L+kUgtaC4IwzBC5iRzQcQCZ+NUwIgIZoANwwiZkywMrTlOKSRfZZNCwlwQhmGETBg14coi24D33aocEaFEBlhERoiIerQM1ynd0B3/QkQm+5m7TETGe1yP91prj4gsFZErwrojwzCOGTFB+H9PoDjgfwJNgY0i8rmIPCIiZxaUP/JERMYGs2AoO+As4Cy33QecgZMDs1IIa/3ssVZ/4BfgAxE5J4S1DKOQVx+5hs2zn2DZxAf8yoy5tx8rpzzMknfv54ymR4rZDurVkRWTH2bF5IcZ1KtjYX/bZmksnXA/K6c8zJh7+0VU33lzZtO2VTNaN2/MmGeeOmo8NzeX6wZdQ+vmjTn3nM5s3rSpcOzZp5+kdfPGtG3VjE/nzinsv+3mG6iXVpOObU+PqK6eSJD/OxFwXQ9nAyOBeOAx4BsgU0Q+FJE73bpzADWDWTMUA5ynqovdNgEYAtQFLg1hrb0ea80F/oJTH+qyENYyjELenrmEvsP8b0J6dmlOwzrVadV/FHc+OYmXHnQqlScnnMrwm3rS7YYX6Hr98wy/qSdJlSsC8NKDA7hj1CRa9R9FwzrV6XFWM7/rl4T8/HzuGXYnUz+exbLlq/jg/fdYs2Z1EZk3/28cSUlJ/LTmF+646+88OvwhANasWc3kSe+z9MeVfDj9E+6+6w7y850SQYMGD+Wj6Z9EREd/nGQuCFQ1R1X/qaodgeo4G8cJOP7hF4HVIvIbTi26gETCB/yd+7NeuAup6mFgH1Au3LWMk5uFP2xgZ/Zev+O9u7ViwqylACxZuZnEyhWpVTWBizo3Zf63a9mVvY/de/Yz/9u19DirGbWqJlC50iksWbkZgAmzltKne2R2lsuWLqFBw0bUb9CA8uXLM+Cqq5k5vWjh3pnTP2bQ4CEA9LtiAF98Ph9VZeb0aQy46moqVKhAvfr1adCwEcuWLgHgnK7dSE6uEhEd/RHjFuUM1HwhIlXcneNeEdksIgP9yImIjBaRTLeNFo/QChEZKyJrReSwiAz1Mf9uEdkmItki8oaIVIjEvavqblX9UFXvUNVmOBvR64EvgfLBrBEJA1zP/bktlMkiEue2KiJyn7teqGWjDSMoUmoksuWP3YXX6dt3k1IjkZTqiWzZ7tVfPZGUGomkb8/y6M8ipUZiRHTJyEgnrc4RF0hqahoZ6elHy6TVASAuLo7EhEQyMzPJSD/SD5CalkpGRtG50SQmJrjmh5eBgzhf1wcBr4hISx9yNwOXA22A1kAf4BaP8eU4FZa/954oIj2Bh3B2pHVxqis/XvI7DYyqbnFL1g8GZgYzJyQD7GE0mwD/BfYAn4awVHvgkNsygdHAA6r6RSh6GYZxbAnVB+w+M+oPPOp+rf8a+BgY7ONlhgBjXAOXDowBhhYMqurLqjofOOBn7jhVXaWqu3D8t0N9yEWa4cEIhRIHXBXHYBbwG3C1qm4NIeB6DXCd++9Tga7Av0QkU1XHh6CbYQRFxvYs0momFV6n1kgiY3sWGTuy6NquUZH+Bd+vJ2N7FqkeO97UGolkeOyIwyElJZUtv28pvE5P30JKaurRMlt+JzUtjby8PLKys6hatSopqU5/4dwt6aSkFJ0bJW4G7mibVplAle5cqzAIeMajuwnO86R1Hn3Lge4+lmjpjnnK+dop+6IlRb9RLwdqikhVVc0Mco0So6qrA0uFHgXREegApAH1VLXA05+Hc1rEF7HuuCf7VHWZ275S1X8BY4FnJARrbhjBMnPBKgZe6kQ4dGpVl+yc/WzLzGbe4rVc2LkpSZUrklS5Ihd2bsq8xWvZlpnNnr0H6NSqLgADL+3IjK9WRkSX9h068uv6X9i0cSMHDx5k8qT3ubR30efQl/buw7tvvwnAh1Mn0/3c8xERLu19GZMnvU9ubi6bNm7k1/W/0KFjp4joFYCxwCXL0/ewbHN2sW15+h6Ad73mxwPZXn1ZQGUfrxXvjnnKxQdpI3zNxc/rHHNC2QHnqeoyP2M78P8wrjZOhEMg1gDV3LajxNoZBvDmyMF0bd+IakmVWD/9MUb+bzbl4py9wetTFzF74Wp6dmnOqqnD2XfgILeMfA+AXdn7eHLcXL4efzcAo16fy67sfQAMe3oKY/9xLRUrlGPuojXMWbQmIrrGxcUx5oV/c3nvi8nPz2fw0Otp0aIlIx//B+3adaBXn8sYcv2N3HT9dbRu3pjkKlUY//ZEAFq0aMkVA66kQ5uWxMXF8dyL/yE21rnPoYMHsuCrL8j880+aNKjD8EdHMOT6GyOicwExCIGqzvt5CJcDJHj1JeC4MwPJJuCcSAumzLSvufh5nWOOBHcPrrDICOBOVa3mZ/x24AWgvuurKeg/E1gMXFqwW3YPZbRS1Q5ea7yM8yQx3o2K8OT4rettHHOsLH3k8ChLX5JvnimLf92VfjC/+PsqHyt0bpicCmQUvojjA94FtFTVX9y+t4AMVX3Ic76ILAL+T1X/517fANysqp295L4GXvd0X4rIBGCjqg53r88HJqhqrRLcZ9SIdC6It4B7gK9E5J/AZpz4uMeARcAcL/lKIlLwJlbE8QH/FfivD+NrGMZxRjDHLHxJqOpeEZkKPCEiN+Ec6OoLdPGxxFvAPSIyC2cTdi/w78L1RcrjuFMFKCcipwAHXRvyFjBeRN7F+QPwCDC+JPcYTSJqgFU1R0S6AaOAp4AqwB/A+8BwH0a1Gc5JEnCeYG4E/gE8F0m9DMOIDjFCYBeE//HbgTdwXJOZwG2qukpEugKfqGq8K/caTvjYCvf6dbevgLkceXjXBcc/fR7wharOFpGngc9xNnlTcDaExwUlckEcB5QpZY3oYi6IyBGqC2LZxqz0QwFcEOVihQ71E4u4IAwHS0dpGEbInGTpKCOOGWDDMELHLHBYmAE2DCNkYiSIMDQL6feLGWDDMELGNsDhYQbYMIzQMQscFmaADcMImVDjgA0HM8CGYYRMmHHAJz1mgA3DCB1zQYSFGWDDMMIgGAeDWWB/mAE2DCNkgqn5ZlFo/jEDbBhGyAhBGOBjoknZxAywYRghY1EQ4WEG2CizeCe7Od7xSHhzwmAuiPAwA2wYRshYEER4mAE2DCNkRCSIHbCZYH+YATYMI2TMBREeZoAN4xhxPCeQD9Wfbi6I8DADbBhG6JgFDgszwIZhhIwEkQ/YfMD+iSltBQzDKLtIkM3nXJEqIvKhiOwVkc0iMtCPnIjIaBHJdNto8bDqInKGiHwnIvvcn2d4jI0QkUMikuPRGkTk5iOAGWDDMEInHAsMLwMHgZrAIOAVEWnpQ+5m4HKgDdAa6APcAoUl6acB7wDJwJvANLe/gPdVNd6jbQjxbiOOGWDDMELGSUcpAdrR80SkEtAfeFRVc1T1a+BjYLCPlxkCjFHVLaqaDowBhrpj5+K4Ul9Q1VxVfQnH5J8f4VuNCuYDNgwjFG4G7qidWCFY+UHAMx7XTYA8VV3n0bcc6O5jbkt3zFOupcfYT6qqHuM/uf2z3es+IrIT2Ar8R1VfCVbpaGMG2DCMUBgLzNiWnZuef7h4wdgYqFe14rte3fFAtldfFlDZxxLx7pinXLzrB/Ye815nkqvrH8CZwBQR2a2qE4vX+thgLgjDMEJGgvyfD3KABK++BGBPELIJQI676y12HVVdraoZqpqvqouAF4EBJb7RKGEG2DCMkCkoSRSo+WAdECcijT362gCrfMiucsd8ya0CWntGReA8qPO1DoByHEUmmwE2DCM8QoiAUNW9wFTgCRGpJCJnA32Bt32IvwXcIyKpIpIC3AuMd8e+APKBu0SkgogUHDf8DEBE+opIshvK1gm4Cydq4rjADLBhGCEThgsC4HagIrAdmAjcpqqrRKSriOR4yL0GTAdWACuBmW4fqnoQJ0TtOmA3cANwudsPcA2wHscl8RYwWlXfjNT9h4sUfXh43FOmlDUMT8pILoiSfD1PSd+Vm54f4LcyViA1uUIqkBGadicuFgVhGEbIiAT+Gm0nkf1jBtgwjDAw6xoOUfMBu2ew1U97zP3Z38/cmiKSJyIPRks/wzDCpyAfcKBm+CbaO+As4GIf/etxTsZcA0zxMX4lzh+H96KnmmEcH7z6yDVcck4LduzKocO1T/uUGXNvP3p2ac6+A4e4+YmJ/Lh2CwCDenXkoesvAuCp/5vHuzOXAtC2WRpj/3EtFSuUY86iNdw75sOo6G7ZKMMj2lEQeaq62Ef7E+epZy8Rifcx7xrgG1XdHGX9DKPUeXvmEvoOG+t3vGeX5jSsU51W/Udx55OTeOlB5xxBcsKpDL+pJ91ueIGu1z/P8Jt6klS5IgAvPTiAO0ZNolX/UTSsU50eZzWLiu6B80A4zfBNaYahTcQJQenr2SkidYAu7rhhnPAs/GEDO7P3+h3v3a0VE2Y5O9slKzeTWLkitaomcFHnpsz/di27svexe89+5n+7lh5nNaNW1QQqVzqFJSud/cuEWUvp0/306CgfXja0k56oG2ARifNuAKr6M/Ajzm7Xk6uBw8AH0dbNMMoCKTUS2fLH7sLr9O27SamRSEr1RLZs9+qvnkhKjUTSt2d59GeRUiMxKrqZ/Q2PaBvgqsAh7yYi9dzxiUAPEUn2mHMN8Jmq/hFl3QzDCBN7CBce0TbAWUBHH60gIPs9oBzQD0BEGgLtMfeDYRSSsT2LtJpJhdepNZLI2J5Fxo4s0mp49e/IImN7FqkeO97UGolkeOyII4lTlj5wM3xzLB7CLfPRDgKo6m/AIo64Ia4BcnHOiBuGAcxcsIqBl3YEoFOrumTn7GdbZjbzFq/lws5NSapckaTKFbmwc1PmLV7Ltsxs9uw9QKdWdQEYeGlHZny1Miq6mQsiPI6HgxgTgRdFpDqOAf5EVaPz59owjkPeHDmYru0bUS2pEuunP8bI/82mXFwsAK9PXcTshavp2aU5q6YOZ9+Bg9wy0onO3JW9jyfHzeXr8XcDMOr1uezK3gfAsKenFIahzV20hjmL1kRHeQnCwJoF9kvUckGIyAjgTlWtFkCuBo5L4lXgDuBqVZ3kR9xyQRhllhMxF8Tu/fnpgUyICCRVjLVcED6I9g44TkQ6++j/3a3thKpuF5H5OJmRcnCyHhmGYZzwRNsAJwLf+Oh/FPinx/VEoAcwTVX3R1knwzAihBT+XwAZwydRM8CqOgIYEaTseI4kWDYMo4xQTK5fDxnDH8fDQzjDMMooMRL4wYwZYP+YATYMI3TMuoaFGWDDMELGXBDhYQbYMIyQCeaQmxlg/1hRTsMwQiacXBAiUkVEPhSRvSKyWUQG+pETERktIpluG+1Zhl5EzhCR70Rkn/vzjGDnljZmgA3DCINgKiL7tXcvAweBmjgFGl4RkZY+5G7GqXzcBmgN9AFuARCR8jhl5t8BkoE3gWluf7FzjwfMABuGETIxQex+Y3zYXxGpBPQHHlXVHFX9GvgYGOzjZYYAY1R1i3uAawww1B07F8eV+oKq5qrqSzgW//wg5pY65gM2DCMUbgZurhDHT0HK9wFe87hugpOsa51H33Kgu4+5Ld0xT7mWHmM/adGcCj+5/bMDzC11zAAbhhEKY90WKvFAtldfFlDZj2yWl1y868v1HvNex+9cjVYinBJgLgjDMEqDHCDBqy8B2BOEbAKQ4xrQQOsUN7fUMQNsGEZpsA4nWVdjj742wCofsqvcMV9yq4DWXpENrb3G/c0tdcwAG4ZxzFHVvTiFF54QkUoicjZOgd63fYi/BdwjIqkikgLcy5HcMV8A+cBdIlJBRApyfn4WxNxSJ2r5gKNEmVLWMMogxyxGVkSqAG8AFwGZwEOqOkFEuuIUZoh35QQYDdzkTn0deLDAjSAibd2+FsAa4EZV/SGYuaWNGWDDMDw5bg4pnAyUtSgI+3AYhnHCYD5gwzCMUsIMsGEYRilhBtgwDKOUMANsGIZRSpgBNgzDKCXMABuGYZQSZoANwzBKif8P2tClXL1VCVsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reading the data\n",
    "\n",
    "infilename = 'graphs-edge-jaccard_data.dat'\n",
    "\n",
    "print('Reading input data %s ...\\n' %(infilename))\n",
    "\n",
    "jacmatrix = np.loadtxt(indirname+infilename, delimiter=' ')\n",
    "\n",
    "# PLOTTING THE DATA\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "\n",
    "im, cbar = heatmap(jacmatrix, label_list, label_list, ax=ax,\n",
    "                   cmap=\"Blues\", cbar_kw={'fraction': 0.0425}, cbarlabel='',\\\n",
    "                   vmin=0., vmax=0.035)\n",
    "texts = annotate_heatmap(im, threshold=0.015, valfmt=\"{x:.3f}\")\n",
    "\n",
    "ax.tick_params(axis='both',length=0,labelsize=15)\n",
    "\n",
    "cbar.ax.tick_params(axis='y',length=12,labelsize=12,color='white')\n",
    "cbar.ax.set_yticks(np.arange(0.,0.035,0.005))\n",
    "cbar.ax.set_ylabel(r\"Jaccard $J$\", fontsize=18)\n",
    "\n",
    "cbar.outline.set_color('white')\n",
    "cbar.outline.set_linewidth(0.2)\n",
    "cbar.dividers.set_color('white')\n",
    "cbar.dividers.set_linewidth(0.2)\n",
    "\n",
    "\n",
    "diroutname = WD[:-4]+'results/'\n",
    "fignameout = 'graphs-edge-jaccard.pdf'\n",
    "\n",
    "plt.savefig(diroutname+fignameout, bbox_inches='tight')\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0059574d",
   "metadata": {},
   "source": [
    "### Visualization empiric -- standalone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd41d9d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tIterating over graph REL\n",
      "\t\tReading file k_core-gender-net_REL-data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tIterating over graph COR\n",
      "\t\tReading file k_core-gender-net_COR-data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tIterating over graph CB\n",
      "\t\tReading file k_core-gender-net_CB-data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tIterating over graph PUB\n",
      "\t\tReading file k_core-gender-net_PUB-data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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pc9rmF/Uo175uDtU1nhOyxTqRq7T9Dxy8A7xux4luLUlaVqx83F7T3QSpcYsXnDypsckNndNolRXgvDdPdyskSZKa5+n17u5sm5/Vo1z7uju7lpIkSZrBDJ3d3dA2v0GPcq11d5S72SVJkjSGobO79jvWt+xaaum6SxtsiyRJ0lAzdHZ3BXBtmd+7U4GImA08pbw9dSoaJUmSNIwMnV1kZgJHlrf7RcS8DsXeDKwGLAa+M0VNkyRJGjojEToj4hERsXbrxdLPNat9+dhxNCPi0IjI8prXoer/Am6iulnoJxGxfdlupYh4I/CRUu5rPnddkiSpu5EIncAC4G9trw3L8n8ds/yL/VSambcDzwH+TvXEoQsi4g6qsTi/DKxEdVr9HZP/CJIkSaNrVEJnYzLzQmAL4LPAlcCKwF3AL4DXAc/KzHunr4WSJEnLvqguXdR02H6DSAeHl6Th5xOJNBNM9olE9nRKkiSpcYZOSZIkNc7QKUmSpMYZOiVJktQ4Q6ckSZIaZ+iUJElS4wydkiRJapyhU5IkSY0zdEqSJKlxhk5JkiQ1ztApSZKkxhk6JUmS1DhDpyRJkhpn6JQkSVLjDJ2SJElqnKFTkiRJjTN0SpIkqXGGTkmSJDXO0ClJkqTGGTolSZLUOEOnJEmSGmfolCRJUuMMnZIkSWqcoVOSJEmNM3RKkiSpcYZOSZIkNc7QKUmSpMYZOiVJktQ4Q6ckSZIaZ+iUJElS4wydkiRJapyhU5IkSY0zdEqSJKlxhk5JkiQ1ztApSZKkxhk6JUmS1DhDpyRJkhpn6JQkSVLjDJ2SJElqnKFTkiRJjTN0SpIkqXGGTkmSJDXO0ClJkqTGGTolSZLUOEOnJEmSGmfolCRJUuMMnZIkSWqcoVOSJEmNM3RKkiSpcYZOSZIkNc7QKUmSpMYZOiVJktQ4Q6ckSZIaZ+iUJElS4wydkiRJapyhU5IkSY0zdEqSJKlxK0x3AyRJg7XycXtNdxMk6WHs6ZQkSVLjDJ2SJElqnKFTkiRJjTN0SpIkqXGGTkmSJDXO0ClJkqTGGTolSZLUOEOnJEmSGmfolCRJUuMMnZIkSWqcoVOSJEmNM3RKkiSpcYZOSZIkNc7QKUmSpMYZOiVJktQ4Q6ckSZIaZ+iUJElS4wydkiRJapyhU5IkSY0zdEqSJKlxhk5JkiQ1ztApSZKkxhk6JUmS1DhDpyRJkhpn6JQkSVLjDJ2SJElqnKFTkiRJjTN0SpIkqXGGTkmSJDXO0ClJkqTGGTolSZLUuJEJnRExJyIOjYiLImJhRNweEedHxLsiYqVJ1v2SiDgxIm6IiPsi4q6IuDwivh4R2wzoI0iSJI2sFaa7AYMQERsB84F5ZdEiYGXgSeW1f0TsmZm39VnvysBxwHPbFi8EVgKeUF6viYh3Z+ZnJ/MZJEmSRtnQ93RGxArAiVSB80bgGZk5G5gF7AfcCWwLHFWj+n9jaeD8MvDozJwDrEoVZn9B9W/4mYjYfhIfQ5IkaaQNfegEDgS2KvMvzszTADJzSWYeC7yhrNsnIvbss+4DyvSszHxzZl7fVveFwHOoej4DeMlkPoQkSdIoG5XQCXBmZp7bYf0xwFVl/oAO63tZr0wv6LQyM28HrihvV+uzbkmSpBljqENnRMwCdi1vT+pUJjMTOLm8fWafu/hzmXY8dR4Rc6mu64QuwVSSJElDHjqBzVj6GS7uUa61bt2IWLOP+r9SprtHxJciYgOAqGwH/Jiqh/Nc6l0zKkmSNCMMe+hcv23++h7l2tet37XUw30J+BSwBHgTcF1E3AncA1wIPA74BLBnZi7uo15JkqQZZdiHTJrTNr+oR7n2dXO6lhojM5dExPuBS4EvUvVqtl+7uQowF5gN3D3ReltuuQt2+tLS9wfvAK/bsd9apGXbysftNd1NkCQtA4Y9dDYqItamGqdzd+BnwIeoTtWvCuwCfBJ4I7BXRDy1dXf7RK09G85780CbLEmStEwa9tPrd7bNz+pRrn3dnV1LPdwRVIHzLGCvzDwnM2/PzJsy8wfAbsAtwCZUp9klSZLUwbCHzhva5jfoUa593Q1dS7WJiM2Afcrbz5S74B8iM28GjixvXxQRMZG6JUmSZpphD51/oLrJB2DLHuVa627KzFsnWPfmbfN/6lHuyjKdBTxqgnVLkiTNKEMdOjNzEXBOebt3pzKl97F1J8OpfVS/pG1+ox7l1mmbX9hH/ZIkSTPGUIfO4ogy3SMiduqwfl+qay5h6anwifhN2/wbOxWIiNksfcrR7zPzrj7qlyRJmjFGJXReRPX88+Nbz1ePiOUiYl/g66XcSZl5evuGEXFoRGR5zWtfl5nXACeWt8+NiG9HxGPLwPArRsSTgfksDbSfaeLDSZIkjYKhHzIpMx+IiOcBZwLzgNMiYhFVoF6lFFsA7F+j+tdQPUJze+CV5bUIWImH/tt9OjP76UWVJEmaUUahp5PMvBrYGvgw1TiaCdxP9dSgdwM7Z+ZtNeq9BdgZOBg4BfgrsCLwANVz2Y8CnpKZ75n8p5AkSRpd0WEkIE2R7TeIdHB4jTqfSCRJo2HxgpMnNTTkSPR0SpIkadlm6JQkSVLjDJ2SJElqnKFTkiRJjTN0SpIkqXGGTkmSJDXO0ClJkqTGGTolSZLUOEOnJEmSGmfolCRJUuMMnZIkSWqcoVOSJEmNM3RKkiSpcYZOSZIkNc7QKUmSpMYZOiVJktQ4Q6ckSZIaZ+iUJElS4wydkiRJapyhU5IkSY0zdEqSJKlxhk5JkiQ1ztApSZKkxjUWOiNi5Yh4VEQYbCVJkma4vgNhRMyOiGeW1+wO69eKiO8BdwA3ArdGxKciYsUBtFeSJElDaIUa27wIOAK4AdiofUVEBPATYAcgyuLVgXcBGwIvr91SSZIkDa06p773KtMTMnPxmHUvAXYs8xcDXynTAF4aEc+o1UpJkiQNtTo9nVsBCZzTYd0BZfo7YKfMvD8iVi5lty3rf1anoZIkSRpedXo6H1Wmf25fGBHLA7tTBdIvZ+b9AJl5L1WPZwA71W6pJEmShlad0Llmmd4zZvm2QOvGopPGrLusTNevsT9JkiQNuTqh894yfeSY5U8p02sy8/ox6+4q0+Vr7E+SJElDrk7ovKpMdxmz/LlUp9bP7rDNWmV6c439SZIkacjVCZ1nUF2f+eaI2BwgIp4HPK2s/0mHbbYs0xtr7E+SJElDrk7o/CLVKfZHAhdFxC3AD6iC6LXACR222YuqF/T39ZopSZKkYdZ36MzMPwGvBBZRBc01y/Q24OWtu9ZbImI9YM/y9sxJtVaSJElDqc44nWTm8RHxc+DZwLpUTyf6UWbe1qH4VsDRZf6ntVopSZKkoVYrdAJk5s3ANydQ7lTg1Lr7kSRJ0vDr+/R6RPymvN7QRIMkSZI0euo+BnM5lg74LkmSJPVU5+71v5bpnYNsiCRJkkZXndC5oEw3HWRDJEmSNLrqhM5vUA2R9MYBt0WSJEkjqs44nScARwK7RcSREbHawFslSZKkkdL3jUQRcQDVIO/bAPsDz4mIE4HfUQ0Qv7jX9pl5ZP/NlCRJ0jCrc/f6t6geadmyBtUTil45gW2TqpdUkiRJM0jdweFjnPeSJEnSg+qEzo0H3gpJkiSNtL5DZ2Ze00RDJEmSNLrqDJkkSZIk9cXQKUmSpMbVvZEIgIhYEXgJ8AyqZ7KvWVbdClwE/Az4XmbeP5n9SJIkabjVDp0R8Szg68B67YvLdB6wHXAg8KmIeF1mnlx3X5IkSRputU6vR8T+wIlUgTPK6xrgV+V1TdvyDYAfR8TLB9FgSZIkDZ++Q2dEbEjVw7kccDfwQWC9zNwkM59cXpsA6wIfAO4qZf+3bCtJkqQZpk5P578AqwCLgN0z86OZ+dexhTLz5sz8GPC0UnYV4G2TaKskSZKGVJ3Q+Uyqx1l+JjMvGK9wZv4GOIzqVPteNfYnSZKkIVcndD6mTE/tY5tTynSjGvuTJEnSkKsTOlt3vN/bxzatspMaokmSJEnDqU7ovKlMn9THNq2yN/UsJUmSpJFUJ3SeTXV95vsj4hHjFY6INYD3Ul0HenaN/UmSJGnI1Qmd/1OmjwbOjYindisYEbsBv2DptZxfqbE/SZIkDbm+r7HMzPMi4nPA24HHA2dGxJ+pBoW/mapHcx1gR+BxbZt+LjN/PfkmS5IkadjUurEnM98REXcD76HqLX0ssMmYYq1HYi4BPpGZ/167lZIkSRpqtR6DCZCZ/wZsDXwZuJKlj71sva4s67Y2cEqSJM1skxrCKDMvBd4CEBErAa0bi27LzPsm2TZJkiSNiIGNm1lC5sMehylJkiTVPr0uSZIkTZShU5IkSY2rfXo9Ip4AvAF4GtWd63MYP8RmZvooTEmSpBmmVgCMiDcBhwErsnRoJEmSJKmjvkNnRDwd+GLbovOAC4BbqcbklCRJkh6iTk/nO8v0H8ALMtPnqUuSJKmnOjcS7Uj1qMuPGTglSZI0EXVC55wyPWuQDZEkSdLoqhM6byzT5QfZEEmSJI2uOqHz1DLdYZANkSRJ0uiqEzoPA+4G3hURcwfcHkmSJI2gvkNnZl4B7A+sA5weEVsNvFWSJEkaKV2HTIqIw8fZ9lJgO+C3EXEx8Adg0TjbZGa+tr8mSpIkadj1GqfzIKqhkXpJqicSbVlevUQpb+iUJEmaYXqFzmsZP3RKkiRJ4+oaOjNz3hS2Q5IkSSOszt3rkiRJUl/qPHtdA/Kb21Zn5eN2me5mzCj37nvKdDdBkqQZqe+ezohYEhEPRMTmfWzz2NZ2/e5PkiRJw6/u6fWY4u0kSZI0xKb6mk7vhpckSZqBpip0rl2md03R/iRJkrQMmUzonFCvZUTMBt5a3v5pEvuTJEnSkBr37vWI+HOXVadGxP3jbL4y8CiqcJvAif01T5IkSaNgIkMmzeuwLIAN+tzXr4BP9bnNhEXEHOBdwIuBjYHFwBXAMcAXMvO+Sda/LvBmYJ9S/6rAzVTPnJ8PfCYzxwvhkiRJM9JEQucRY94fSNVr+SPgHz22S+Ae4Ebgl8AZmdnIjUQRsRFV8JtXFi2i6mV9UnntHxF7ZuZtNet/GfA1YPWy6B7gPuAx5bUX8D/0/veQJEmascYNnZn56vb3EXFgmf33zLy0kVb1ISJWoDptP48q4B6QmadFxHLAvsDXgW2Bo4Bn16h/X+BoqksEvgZ8rvW5S+/qNsALAXs5JUmSuqjzRKIPlenNg2zIJBwIbFXmX5yZ5wJk5hLg2BI+jwb2Kb2dp0+04ohYD/gqVeB8V2Ye1r4+M+8Efl5ekiRJ6qLvu9cz80PldUsTDaqh1fN6ZitwjnEMcFWZP6DPut8GPAJYAHy2XvMkSZI01YPDD1REzAJ2LW9P6lSmXEd6cnn7zD530QqpRzV1PaokSdJM0Pfp9Yh46mR2mJlnT2b7MTZjaXC+uEe51rp1I2LNzLx1vIojYmNg/fL2wojYCng/sAewJvA34Bzg85l5Tp3GS5IkzRR1rumcT/3HWWbNfXazftv89T3Kta9bHxg3dAJPaJvfFTgEWAm4m+ru9Q2AlwL7RsQhmfmRCbVYkiRpBqp7ej0m8RqkOW3zi3qUa183p2uph3pE2/xHgBuAZwCrZeZcYAuqAB7AhyPiRROsd6kH7mPJ5ec++Mpb/tJ3FZIkScOgTq/jHhMoMxt4IvAKYDvgF1Q9hUtq7G+6tAfyoLoz/jetBZl5aUQ8F7gSWJfq832/rz2ssBLLbbrLAJoqSZK0bOs7dGbmWRMs+lPgsIj4ANUwSwdl5oHjbNOvO9vmZ/Uo177uzq6lutd9envgbMnMhRHxJaqe0K0jYp3M/OsE65ckSZoxGr97vVzr+GPglRHxkgFXf0PbfK/Hcravu6FrqYdqvw70Dz3KtQ+Qv9EE65YkSZpRpmrIpG9RnaJ+/YDr/QNLT9lv2aNca91NE7lzvbiU6vnt42m/TtVhlSRJkjqYqtDZGpx9m0FWmpmLqIYtAti7U5mICKpnowOc2kfd9wCt4Z0261F089YmwNUTrV+SJGkmmarQ2boTfKJ3jvfjiDLdIyJ26rB+X2CTMn9kn3V/s0z3jIjtxq6MiNWAN5W352Xm3/qsX5IkaUaYqtD56jK9roG6jwAuojrNfXxE7AkQEctFxL7A10u5k8Y+dz0iDo2ILK95Her+DvDr9rrLs9yJiM2AH1Hdub4E+PfBfzRJkqTRMMiB2h8mIp4AvAfYn+r0808GvY/MfCAingecCcwDTouIRVSBepVSbEFpQ791L4mI5wOnU51GPw1YFBH3A3NLsfuBN2fmGZP6IJIkSSOszmMw/zyBYssBa/DQ0+k3AR/vd38TkZlXR8TWwLuBFwEbU4XBS4DvAl/IzPtq1n1TObX+FuBlVE8qWpXq+s0zgM9mZq9HcEqSJM14kdnfDdcRUWeA93OAV2fmH2tsO7Ji1tx0cPipde++p0x3E2aclY/ba/xCkqRl3uIFJ0/qyZJ1Tq8fMX4RllANrv5n4KzM/F2N/UiSJGlE1Hki0avHLyVJkiQtNVV3r0uSJGkGM3RKkiSpcYZOSZIkNW5S43SWR0xuA/w/YG2qoYR63tmUmR+ezD4lSZI0fGqHzog4EDgE2KjPTQ2dkiRJM0yt0BkRHwPexzi9mkVOsJwkSZJGVN/XdEbETsD7y9ufUZ1e3668T2B54JHAs6ieTR7AL4D1MtNrSCVJkmagOiHwjWV6DfDszPw91SMnAcjK3zPzlMx8AfBmYDfg5IhYabINliRJ0vCpEzqfTNWj+fnMfGC8wpn5FeB4YGvgTTX2J0mSpCFXJ3SuV6aXtC178HnsEbFih22+TXWa/WU19idJkqQhVyd0tkLlzW3LFrbNP7LDNteV6eNq7E+SJElDrk7o/FuZrt627K/A4jK/WYdtWr2jc2rsT5IkSUOuTuhsnVZ/YmtBZt7XtrzTKfRXlekNNfYnSZKkIVcndP6c6vrMPcYsP7Ysf01EfCgitoiIHSPiy8BLqW4+OmlSrZUkSdJQqhM6TyjT50RE+yn2zwFXlzr/A/g9cC7whrL+NuDjtVopSZKkodZ36MzMS6h6OV9I2xONMnNRWX4OVY9n++tiYM/MvO5hFUqSJGnk1XoMZmae1WX5NcBTImJTYItS/5WZuaB+EyVJkjTsaoXO8WTm5cDlTdQtSZKk4eOz0CVJktQ4Q6ckSZIaZ+iUJElS4wydkiRJapyhU5IkSY0zdEqSJKlxhk5JkiQ1ztApSZKkxnUNnRFxeER8IyLWm8oGSZIkafT06uk8qLwe0b4wIpZExAMRsXmD7ZIkSdIIqXt6PQbaCkmSJI20XqHzrjJdeyoaIkmSpNHVK3ReWaavjghvOJIkSVJtK/RY9wNgG+AAYK+I+BNwf9v6b0bEXZ027CEzc88+t5EkSdKQ6xU6Pw3sDewCrFteLQHs0Md+smyT/TZQkiRJw69r6MzMeyLiacC+wD8BGwArA0+jCo8XsvS6T0mSJKmrXj2dZOYDwHfLC6iGTCqzB2XmpQ22TZIkSSPCG4QkSZLUuJ49nV1sXKbXD7IhkiRJGl19h87MvKaJhkiSJGl01enpfIiI2AV4BrAVsGZZfCtwEfCzzDx3svuQJEnScKsdOiPi/wH/C2zXpciLgEMi4kLg4Mz8fd19SZIkabjVupEoIvYEfkUVOKO8HgD+Wl4PtC1/EnBeROwxiAZLkiRp+PQdOiNiLeB7VGN2JnA4sDMwOzPXz8z1gdnATsA3gCWl7PERsWbnWiVJkjTK6pxefxswl+qRmC/KzJ+MLVDG9zwfOD8ifgCcULZ5G3Bo3cZKk7XycXtNdxMkSZqR6pxefzZVD+dXOgXOsTLzp8CXqU61P6fG/iRJkjTk6oTOTcr0hD62aZXdpFchSZIkjaY6oXPVMr2jj21aZVftWUqSJEkjqU7o/FuZbtnHNq2yN9fYnyRJkoZcndD5S6rrM98dESuPVzgiVgLeTXUdqAPFS5IkzUB1Que3ynQL4GcRsXG3ghGxEXAyS3s6D6+xP0mSJA25Os9ePzkijgP2BXYFroiI+VSDxd9M1aO5DtU4nbsDy5dN/y8zTx1AmyVJkjRk6j4G81XAYmA/qlD59PIaK8r0u8Cra+5LkiRJQ67WYzAz877MfAXVmJ0/Be5m6WMvW6+7y7p9MnP/zLxvME2WJEnSsKnb0wlAZp4EnBQRy1ONwdl6zOWtwJ8zc/Ek2ydJkqQRMKnQ2VLC5ZWDqEuSJEmjp9bpdUmSJKkfhk5JkiQ1ztApSZKkxhk6JUmS1DhDpyRJkhpn6JQkSVLjDJ2SJElqnKFTkiRJjTN0SpIkqXGGTkmSJDXO0ClJkqTG1X72ekQ8AXgD8DRgE2AO44fYzMyBPO9dkiRJw6NWAIyINwGHASsCMdAWSZIkaeT0HToj4unAF9sWnQdcANwKLBlQuyRJkjRC6vR0vrNM/wG8IDPPHlxzJEmSNIrq3Ei0I5DAxwyckiRJmog6oXNOmZ41yIZIkiRpdNUJnTeW6fKDbIgkSZJGV53QeWqZ7jDIhkiSJGl01QmdhwF3A++KiLkDbo8kSZJGUN+hMzOvAPYH1gFOj4itBt4qSZIkjZQ643QeXmYvBbYDfhsRFwN/ABaNs3lm5mv73ackSZKGW2RmfxtELKEaMunBRWPed92UKnR6A1IRs+bmcpvuMt3NkCRJGtfiBSdP6imUdQaHv5aJhUxJkiQJqBE6M3NeA+2QJEnSCKtz97okSZLUF0OnJEmSGlfnms6OImI2sGZ5e2tm3jWouiVJkjTcJtXTGRFbR8RXI+LPwB3A1eV1R0T8OSL+JyK2nnwzJUmSNMxqh86I+ARwIXAwMI9qSKT21zzgdcBvIuLjk22oJEmShlet0+sR8TngLVThEuAy4FfATeX9usBOwGalzHsiYtXM/JdJtVaSJElDqc4TiXYB3ko1VudlwOsz8xddyu4KfBXYHHhrRByTmb+aRHslSZI0hOqcXn9Dmf4F2LVb4ATIzHOApwDXlEX/XGN/kiRJGnJ1QudTqXo5P5GZt41XuJT5FNVp9qfW2J8kSZKGXJ3QuW6ZXtDHNueP2XbgImJORBwaERdFxMKIuD0izo+Id0XESgPe1/9ERJbX1YOsW5IkaRTVuZHoPmDl8pqoVtn7auxvXBGxETCf6o55gEVln08qr/0jYs+J9MxOYF97AK+fbD2SJEkzSZ2ezmvLdO8+ttmrTK/pWaqGiFgBOJEqcN4IPCMzZwOzgP2AO4FtgaMGsK9ZwNeBB+ivp1eSJGlGqxM6T6W6PvMdEbHDeIUjYlvgHVTXgZ5SY3/jORDYqsy/ODNPA8jMJZl5LEtvfNonIvac5L4+BjyW6hrVSyZZlyRJ0oxRJ3T+N3APsCpwZkR8MCI2GFsoItaPiP8AzgJml20+N4m2dnNgmZ6Zmed2WH8McFWZP6DuTiJiZ+BtwBXAR+vWI0mSNBP1HToz8zqqpxBBFTwPAa6NiL9ExIURcUFE/IVqSKUPAatR9XK+NjOvH1C7gQdPd+9a3p7Upb0JnFzePrPmflYGDqfq4X19Zt5Tpx5JkqSZqtYTiTLz6Ii4Ffga8OiyeIPyGus64HWZ2cSp9c1YGpwv7lGutW7diFgzM2/tcz8fLPv638w8q89tJUmSZrxaoRMgM0+OiE2AFwDPALYE1iyrb6UKej8DTsjMBybZzm7Wb5vv1Yvavm59qvZNSLkm9T3AX4F/7at1kiRJAiYROgFKmPxeeU2HOW3zi3qUa183p2upMcqd8YdT/Tu9LTP/0VfrxvPAfSy5fOllqLHWo4m1NxzoLiRJkpYFkwqdM8D7gG2AH2fm/w289hVWYrlNdxl4tZIkScuaOnevL0vubJuf1aNc+7o7u5ZqExGbAx8AFgJv6r9pkiRJahn2ns4b2uY3AH7fpVz7DU43dCkz1peAlajuzr8tIlYbs771bxdt6+7NzPsnWL8kSdKM0bWnMyIWl9cDXZbXeQ36hqI/AEvK/JY9yrXW3dTHnesbl+nHqXpHx772L+sf07bszROsW5IkaUbpdXo92l7dltd5DUxmLgLOKW87PpYzIoKlj+E8dZD7lyRJ0sT0Or3+oT6XT5cjgKcAe0TETpl53pj1+wKblPkjJ1ppZs7rtT4ivkX1NKRrxisrSZI003UNnZnZMVx2Wz6NjgDeTvX89eMj4sDMPD0ilgNeDHy9lDspM09v3zAiDqW6ZhNg48y8emqaLEmSNLMM+41EZOYDEfE84ExgHnBaRCyiunRglVJsAUuvwZQkSdIU63vIpIh4anmt2sc2q7S263d/E1F6KLcGPkz1JKQE7gcuBN4N7JyZtzWxb0mSJI0vMrO/DSKWUN0xvnVmXjrBbR4LXAksycyh710dlJg1Nx0cXpIkDYPFC06e1A3hdQeHr7vTgd69LkmSpOEwVU8kau1n8RTtT5IkScuQqQqdG5Xp7VO0P0mSJC1Dxr2+MiIe02XVehGxcJzNVwYeC3yE6uaeS/prniRJkkbBRG7quarDsqDe030mPDi7JEmSRsdEQme3m3/6uSnoHuDzmXl4H9tIkiRpREwkdL56zPtvUp0q/wBwfY/tkips3ggsyMzxTsVLkiRpRI0bOjPziPb3EfHNMnvCRMfplCRJ0sxWZ6D2Pcq007WekiRJ0sP0HToz86wmGiJJkqTR1XfojIjlgV3L299lZs+xNyNiDarnogP8PPt97qYkSZKGXp3B4Z8PzAe+D9w/gfL3lbJnAs+usT9JkiQNuTqh8wVlelxmLhqvcClzLNUQSy+qsT9JkiQNuTqhc0eq4ZDO7GObVtmdauxPkiRJQ65O6NywTP/cxzZXl2m3R2pKkiRphNUJna1t+rkhqFV2xRr7kyRJ0pCrEzr/VqaP72ObVtlba+xPkiRJQ65O6PwN1U1B+/WxzcvL9Hc19idJkqQhVyd0nlCmz42IV41XuJR5LtUp9h/U2J8kSZKGXJ3QeRTwR6rezm9GxBcj4nFjC0XE4yPiy8A3qQLn1WVekiRJM0ydx2A+EBEvBH4BzAXeCLwxIm4GbizF1gMeVeYDuBN4YWZOZDB5SZIkjZg6PZ1k5iVU43WeTxUqA1gH2Ka81mlbfgGwQ2b+fvLNlSRJ0jDqu6ezJTOvBHaKiD2B5wDbAY8sq28BLgROzMwzJt1KSZIkDbXaobMlM08HTh9AWyRJkjSiap1elyRJkvph6JQkSVLjJn16HSAilgceAaxKdfNQV5l57SD2KUmSpOFRO3RGxNrAW4EXAJszsV7TnMw+JUmSNJxqBcCIeDLwfaq71Xv2bEqSJEl9h86IWAv4IbAWsBD4X+AfwKFUPZkHA2sCTwKeB6wCnAN8YxANliRJ0vCp09P5FqrAeS+wS2ZeEhFbUIVOMvPBR11GxHrA0cBTgXMz872TbrEkSZKGTp27159F1aN5eHkyUVeZeSOwD/An4N0R8fQa+5MkSdKQqxM6H1emp7Uty9ZMuZN96YrMu4HPUl37+c819idJkqQhVyd0rl6m17Qtu6dtfk6HbS4o051q7E+SJElDrk7oXFim7deD3to2P6/DNquU6aNq7E+SJElDrk7o/GOZPqa1IDP/AdxU3u7RYZvdyvSuGvuTJEnSkKsTOs8r0x3GLD+Z6rrN90TE41sLI2Jn4F+prvs8v04jJUmSNNzqhM5TqMLli8YsPwx4gOoU+iURcX5EXAr8HFijlPlczXZKkiRpiNUNnUcCv4qIjVsLM/Ni4I3AYqrrPbcHngi07mY/NDNPnlxzJUmSNIz6Hhw+M+8HDuqy7hsR8YuyfotS/5XAtzPzgk7bSJIkafTVevZ6L5l5OfD+QdcrSZKk4VXn2euHldn5mfmjAbdHkiRJI6hOT+fby/T0QTZEkiRJo6vOjUR/L9PrB9kQSZIkja46ofPKMl1/kA2RJEnS6KoTOo+lGqfzZQNuiyRJkkZUndD5FWABsH9EHDzg9kiSJGkE1bmRaD3gdcDhwFcjYj/gO8DvgNuoBofvKjOvrbFPSZIkDbE6ofNqqueoQ3WafY/ymoisuU9JkiQNsboBMLrMS5IkSQ9TJ3S+euCtkCRJ0kir8+z1I5poiCRJkkZXz9AZER8ss1/OzFumoD2SJEkaQeP1dB5KdfPP94CHhc6IWB/4KJCZ+dqBt06SJEkjoc44ne0eARxUXpIkSVJHkw2dkiRJ0rgMnZIkSWqcoVOSJEmNM3RKkiSpcYZOSZIkNc7QKUmSpMZN9IlEb4qImzssf1Rrpm0g+Z4y88MT3KckSZJGRGRm95URS6gGhx+YzFx+kPUNs5g1N5fbdJfpboYkSdK4Fi84OSaz/UR6Oie1gzEGGmAlSZI0HMYLnXtMSSskSZI00nqGzsw8a6oaIkmSpNHl3euSJElqnKFTkiRJjTN0SpIkqXGGTkmSJDXO0ClJkqTGGTolSZLUOEOnJEmSGmfolCRJUuMMnZIkSWqcoVOSJEmNM3RKkiSpcYZOSZIkNc7QKUmSpMYZOiVJktQ4Q6ckSZIaZ+iUJElS4wydkiRJapyhU5IkSY0zdEqSJKlxhk5JkiQ1ztApSZKkxhk6JUmS1DhDpyRJkhpn6JQkSVLjRiZ0RsSciDg0Ii6KiIURcXtEnB8R74qIlWrWuUFEvCkijouIP0bE3eV1VUR8NyKePujPIUmSNIoiM6e7DZMWERsB84F5ZdEiYHlg5fJ+AbBnZt7WR50bAtcA0bZ4UXm/atuyw4HXZ+bivts9a24ut+ku/W4mSZI05RYvODnGL9Xd0Pd0RsQKwIlUgfNG4BmZORuYBewH3AlsCxzVZ9XLUwXM04EDgQ1KvasBWwA/LOVeAxw6qQ8hSZI04oa+pzMiXgv8b3n75Mw8d8z6lwNHl7f/lJmnT7DeucBjM/M3XdYH8FNgb2Ah8MjMvKevttvTKUmShsSM7+mk6oUEOHNs4CyOAa4q8wdMtNLMvL1b4Czrk+rUOlS9n5tNtG5JkqSZZqhDZ0TMAnYtb0/qVKaEw5PL22cOuAntPZvLD7huSZKkkTHUoZOqd7H1GS7uUa61bt2IWHOA+9+9TO8DrhhgvZIkSSNl2EPn+m3z1/co175u/a6l+hARGwP/XN4em5l3DKJeSZKkUbTCdDdgkua0zS/qUa593ZyupSYoIlYFjqO6Q/4W4H21KnrgPpZcvvQy1Fjr0cTaG062eZIkScucYQ+dU64M0XQ0sD1wP7B/Zt5Qq7IVVsK71yVJ0kww7KfX72ybn9WjXPu6O7uWGkdELA98B3gB8ADwisw8tW59kiRJM8Wwh872HsYNepRrX1erV7IEzqOAlwKLgVdm5vfq1CVJkjTTDHvo/AOwpMxv2aNca91NmXlrvztp6+Hcj6WB89h+65EkSZqphjp0ZuYi4Jzydu9OZcqTg/Yqb/s+FV4C59HAy1gaOI/pv7WSJEkz11CHzuKIMt0jInbqsH5fYJMyf2Q/Fbf1cL6U6hrO/Q2ckiRJ/RuV0HkREMDxEbEnQEQsFxH7Al8v5U4a+9z1iDg0IrK85o1Z17qG82UsvWnIU+qSJEk1DP2QSZn5QEQ8DzgTmAecFhGLqAL1KqXYAmD/PqveleoaToAEvhARX+hR/u2GUkmSpM6GPnQCZObVEbE18G7gRcDGVGNoXgJ8F/hCZt7XZ7XtvcArAuuMU37VPuuXJEmaMSIzp7sNM1bMmpsODi9JkobB4gUnx2S2H4VrOiVJkrSMM3RKkiSpcYZOSZIkNc7QKUmSpMYZOiVJktQ4Q6ckSZIaZ+iUJElS4wydkiRJapyhU5IkSY0zdEqSJKlxhk5JkiQ1ztApSZKkxhk6JUmS1DhDpyRJkhpn6JQkSVLjDJ2SJElqnKFTkiRJjTN0SpIkqXGGTkmSJDXO0ClJkqTGGTolSZLUOEOnJEmSGmfolCRJUuMMnZIkSWqcoVOSJEmNM3RKkiSpcYZOSZIkNc7QKUmSpMYZOiVJktQ4Q6ckSZIaZ+iUJElS4wydkiRJapyhU5IkSY0zdEqSJKlxhk5JkiQ1ztApSZKkxhk6JUmS1DhDpyRJkhpn6JQkSVLjDJ2SJElqnKFTkiRJjTN0SpIkqXGGTkmSJDXO0ClJkqTGGTolSZLUOEOnJEmSGmfolCRJUuMMnZIkSWqcoVOSJEmNM3RKkiSpcYZOSZIkNc7QKUmSpMYZOiVJktQ4Q6ckSZIaZ+iUJElS4wydkiRJapyhU5IkSY0zdEqSJKlxhk5JkiQ1ztApSZKkxhk6JUmS1DhDpyRJkhpn6JQkSVLjDJ2SJElqnKFTkiRJjTN0SpIkqXGGTkmSJDXO0ClJkqTGGTolSZLUOEOnJEmSGmfolCRJUuMMnZIkSWqcoVOSJEmNM3RKkiSpcYZOSZIkNc7QKUmSpMYZOiVJktQ4Q6ckSZIaZ+iUJElS4wydkiRJapyhU5IkSY0zdEqSJKlxhk5JkiQ1ztApSZKkxhk6JUmS1DhDpyRJkhpn6JQkSVLjDJ2SJElqnKFTkiRJjRuZ0BkRcyLi0Ii4KCIWRsTtEXF+RLwrIlaaZN3rRMRnIuLyiLg7Im6NiJ9HxMEREYP6DJIkSaMqMnO62zBpEbERMB+YVxYtApYHVi7vFwB7ZuZtNereHjgFWKssWgisAqxQ3p8CPC8z7+u77llzc7lNd+l3M0mSpCm3eMHJk+poG/qezohYATiRKnDeCDwjM2cDs4D9gDuBbYGjatQ9F/gxVeC8DNghM+cAs4G3APcDewH/PdnPIUmSNMqGPnQCBwJblfkXZ+ZpAJm5JDOPBd5Q1u0TEXv2Wfe7gXWBu4F9MvOCUvd9mfkl4JBS7vUR8YTJfAhJkqRRNiqhE+DMzDy3w/pjgKvK/AF91t0qf0xmXtVh/ReoTrcvD+zfZ92SJEkzxgrjF1l2RcQsYNfy9qROZTIzI+Jk4I3AM/uoe1PgMePUvTAifg48q9R9SKdy3Tx6o3m882vf7GeTZca5PziKXV74yuluRt/e/fpXT3cTJEkzXN7yF2LtDae7GVNu2Hs6N2PpZ7i4R7nWunUjYs0J1r1lh+171b35BOsdCef+oO9LZCVJEpB/v266mzAthj10rt82f32Pcu3r1u9aanJ1rx4Rq02wbkmSpBllqE+vA3Pa5hf1KNe+bk7XUoOpe+EE6+e6y36/8J07rt8e/P8G3DLR7afZ2u/ccf1haaskScuStZf89pShO4ZGxMmZuXfd7Yc9dA61MvySJEnSyBv20+t3ts3P6lGufd2dXUtNXd2SJEkzyrCHzhva5jfoUa593Q1dS02u7jsyc8Kn1iVJkmaSYQ+dfwCWlPkte5RrrbspM2+dYN3td6xPpO5LJ1ivJEnSjDPUoTMzFwHnlLcdL2yNiKB6VCXAqX1UfwVw7Th1zwaeUqNuSZKkGWWoQ2dxRJnuERE7dVi/L7BJmT9yopVmZraV3y8i5nUo9mZgNWAx8J2J1i1JkjTTjErovAgI4PjW89UjYrmI2Bf4eil3Umae3r5hRBwaEVle8zrU/V/ATVQ3C/0kIrYv260UEW8EPlLKfS0zrxj0B5MkSRoVQz9kUmY+EBHPA84E5gGnRcQiqkC9Sim2gBrPRs/M2yPiOcApVE8cuiAi7iz1rliKnQq8Y1IfQpIkacSNQk8nmXk1sDXwYaobgBK4H7gQeDewc2beVrPuC4EtgM8CV1KFzbuAXwCvA56VmfdO8iNIkiSNtKguXZQkSZKaMxI9nZIkSVq2GTolSZLUOEOnJEmSGmfolCRJmoSI+FYZfvFb092WZdnQD5mkiYmIFwDbAL/NzBOmtTGSJGnGsadz5ngBcEiZSpKkwbkRuLxM1YU9nZIkSZOQme8H3j/d7VjW2dMpSZKkxhk6p0lE7B4Rx0XE9RFxb0TcEhGnR8SrI2L5DuXHvUg5Ig4qZa4es58EDiyLDmx73nzrtXuHuraMiK9FxJURsSgiFkbE7yPiYxGxdpf9t55lP7+8f3FEnBoRN0fEkog4dOL/QpIkLRURq0bEYyNimTtL2+0YHRHzy/JDo/K6iDgvIu6IiDsj4tyIeGWPeq8u2x8UEStFxL9GxO8i4q6IuD0izoiIvSfQvl0j4qiIuCYi7inb/joi3hsRq433mUrbD46IX0TE31tt6vffaZn7j5sJIuIwlj6vPYHbgTWAp5fXKyPiBZl55wB2dx/wV2Au1TPj7yn7G1umvX3vAT7O0j9KFlE9/nOr8np1RDw7Mxd022lEfAZ4J9Xn+wewZLIfRJI0M0XE6sBvgMcCV0XER4FvZ+b909uyCVse+AHwfOABquPqHGBnYOeIeHxmHtJj+9WAs4GdqB7zfS+wOrAHsHtEHJyZh4/dKCKWo3qM99vaFi8EZgM7lNerI2KvzLymy74DOA54MdWx/HZqHtPt6ZxiEfEWlgbOrwHrZ+YjqELhO6h+GJ8OfH0Q+8vMX2bmusCxZdGxmbnumNcv29r3WuCTVL8Q/w6sl5mzgVnAk4AzgPWAH3X76wjYnipwfhJYJzPXpPoB/+YgPpMkacbZiypwAmwMfAO4PCJeExErTl+zJuzNwO7AQcDqmTkX2BA4saz/j4h4fI/tPww8mupm4NmZOQd4IvArqlD4uYiY22G7D1EFzptLG9Yq265KFVgXAJsC3y8BtZMXUYXldwOPKMf0ucAp437qMQydUygiVqX6AQD4bma+ITNvAsjMuzLzv6nCGsDLImL7KW7fHOC/ytuXZOZ/trVvcWZeSPWLfyHVD//BXapaDTgsM9+XmX8r29/b468oSZJ6md1h2TCFz0cAL8zMIzLzboDMvA7YF7iBKo+9tMf2s4B/yswftnp3M/Ny4HlUZzBXA57TvkFEzKO6uelu4JmZ+eXMvLVse39mzgeeBlwHbFfq6mQ14J2Z+ZnMvKNsvzAz+75T39A5tZ4BrFnmD+1S5sssHXLhFU03aIwXU53mX5CZHf+CycwHgO+Wt3t1qWcJVS+nJElNG4bweU5mnjl2YWbey9Iew617bP+9zLysw/Z/A87tsv1BVKf1T87M33WqtFzGd0J52+2Yfhvw1R5tmzCv6ZxaTyrTv2TmFZ0KZObiiDgD2L+t/FTZtUw3i4ibepRbtUw36rL+j5l58+CaJUnSuFrh8z+WwWs+z+ux7oYyXbNHmTrbt47pzxznmN66VK7bMf38zLyvy7q+GDqn1qPK9Ppxyl03pvxUWb9MVymv8czqsnxKA2e5k/GpVBdkS5JGz9P7KLsshs9eNwY/UKa9emjrbN86ps+m8+UJYzV+TDd0ql1rqKZjM3O/SdSzeBCN6cNXgddM8T4lScu2Vvh8Ad2vVxxlrWP6JzPzfZOoZ2DHdK/pnFqtvxYePU651vr2vy5af8n06oHsdOdaP1rd79262JdVL5zuBkiSllnPLUMuzTTL3DHd0Dm1LijTR0fEEzoVKAPD71Hent+26rYy3bBH/Tv1WNcaUyt6lDmnTLePiPV6lFvWnDh+EUnSDHVy667rGaZ1TP+niJjIJXON8/T61PoZ8HdgLaq71zvdnf4Gll6H8d225a07z3aIiA0z8y/tG0XEZlRjaXXT+oVbo0eZ44BPlTKHRcQrMjM7FSzjea2emf/oUd9UOZjq3+qp090QSVIjdgd26XObvwD/CTxs0PQZ4nDgPcDaVMM1vrdbwYhYCVgpMxc22SBD5xTKzLvLoyC/ALw8Iu4ADsnMv0bELKrw1Bon89gyLmbLiVRPEVgN+L+IOCgzLy9DQ+xDNdTSXcDKXXZ/cZk+JSKe2GXohX9ExL8A3wL2A+ZGxIeo7lxbUoLmpmV/BwMfA46q828xSOUC8ZPLS5I0YsojFycaOlth85tlSKIZKTP/FBEfoQqc7ylnMD+VmRfDgzfhbkl1vevBVB1hv2iyTYbOKZaZX4yITaiePvQG4PUR8Q+qx2G1/j/OBF43ZrvbSyD8OtVd2pdFxJ1UIXMlqqcSHAV8scuuj6f6JXwk8IeIuIUqpALsl5m/Kvs5ogxi/zngWeV1b0QspHrkVvvdcR17QSVJmgaGzYf7CFW2+A/gVcCrIuJuqqcOrsHSm41gCo7pXtM5DTLznVTDPxxP9Vz01aiGQziT6i7sZ3R67npmfgN4NtWjKO+g+kG6Angf1VMF7hq7Tdu2t1Gdfj6GasimuVQXF2/EmJuTMvN/qHo0/4vqtP69VD+cC6muS/0C1UD37af/JUmaDn8B3gg8PjP/x8C5VFY+SDVw/JeBP1DdjT6X6l6RXwKfBp6cmed0rWhAossle5IkScuEiHgpcOyYxfZsDhlDpyRJWqZFxFyqM28bYdgcWoZOSZK0zIuI2cBjgD8N6rGMmlqGTkmSJDXOG4kkSZLUOEOnJEmSGmfolCRJUuMMnZIkSWqcoVOSJEmNM3QOuYh43HS3QZKkqeSxbzgZOodYRLwd+OB0t2OYRcQTIuK0iFhtutsiSRqfx77pMYjjpaFzSEXEm4AXA6+b7rYMs8y8AjgC+KnBU5KWbR77ps8gjpcODj+EImI34IfAtpl5bZcyqwKHAusAawNrAadl5gemqp3TISK2A94P7AK0frjPBD6cmX/ssd3XgDUz8yXNt1KS1K9ex76ZesxrFxH/Arw+MzfvY5sVgVcDrwI2Bh4AbgI+k5nHddmm9vHSns4hU36xjgQ+1C1wFisD9wErAPsAOwO3Nt/C6RMRLwHOo/rs22TmhsCTgccDF0bEjj02fyewS0S8qvmWSpL6MYFj30w85i0fERtFxEERcT7wWWBWH9uvA/yCKqwfBjwmM+cBBwBv7XE8rH28tKdzyETEh6n+KnnsRJ49W35R76D6Rdw2M3/bbAunR0SsD1wK3Ev1b7Owbd2jgT9R/Ts8PjP/0aWONwIfL9v/vfFGS5ImZKLHvhl0zDsAOIQqWP8a2AB4PnBNCY7jbT8LOAt4HPCkzPxT27q3AZ8CrsrMzbpsX+t4aU/nEImINYB3AJ+fSOAsdqH65bsN+H1DTVsWfAqYCxzRHjgBMvM64EdUp1ze26OOb1D9pfz2phopSepPn8e+GXHMy8wjM/OxmblDZr4Z+G2fVXwdeBLwr+2Bs3g9Vc/xRj22r3W8NHQOlzcAqwLf6WObPcr0rMxcMvgmTb+ImA28qLz9QZdi3y/TAyIiOhUoX2ZHUJ1WWH2wrZQk1dTPsW/kj3mTVe59eAVwOVV4HOurwELgK93qqHu8NHQOlwOAX2XmDX1s0/oFPLOB9iwr9qb6QlpC979sf1em6wPb9ajreGANqrsjJUnTr59j30w45k3WIWV6fHa4xjIzv5CZczLzXePU0/fx0tA5JCJiM2Bz4PQ+tpkNtG6emd9As5YV25bpzZl5V5cyf26b7xU6f011jczzB9EwSVJ9/Rz7ZtAxr7bSK7lPeXv+JKvr+3hp6Bwee5bpr/rYZldgReDvwEUDb9Gyo/Vkitu6FcjMe4B7ytsn9Ci3hOoX6RkRscrAWihJqqOfY99MOeZNxp5U17wCXD+ZiuocLw2dw2OXMr24j23ar215sAs9IjaMiG9HxM8j4pyIeMPAWjk9Hl2md45TrrV+/XHKLaAadmLLyTRKkjRp/Rz7ZsoxbzJ2aZu/JSLmRcTxEfGniLg5In4aEc/so76+jpeGzuGxFXBvZv6lj20edm1LuYD4COBLVHdyPwb40pA/jWdOmd4/TrnW+rnjlGvdybdV7RZJkgahn2PfTDnmTUZ7p8ujgc8B78/MxwLzqG4uOiUiPjPB+vo6Xq4wfhEtIzYG/jrRwhExB9i+vJ1flj0ZeBfwgsy8IyJ+SPVDdzfV0AcDERFPAH4MrDSgKj+ZmV3vogNml+kD49TTWj/eaYDWKYctxmuYJKlREzr2zbBj3mQ8qm3+EODAzLweIDMXAe+IiCcB74yIazLz8+PU19fx0tA5BMrF0asBV/ax2VOo/n//BlwSEU8HDgZekZn3ljLHU/0CfrKPcT/HVZ7P2vW6yQZMdFiM1hfCeOH0b2X6yHrNkSRNVp/Hvpl0zJuM9rvVr2wFzjG+A+wGfCwijsjM23vU19fx0tA5HFqnAe7pWeqhWqcZzgbeRPUX4IGZ+eAp6Mw8kuqxYsOu2x3rY61Ypgt7lqr+CgZwrE5Jmj79HPtm0jFvMtqPf6d2KXNJma4G7Av8b4/6+jpeek3ncGj10I13zWK71i/gc4F/Bo5mdP/IuHmC5VrXcv5jnHKtL7g5PUtJkprUz7FvJh3zJuOmtvluPci3tM3vNk59fR0vDZ3DofWXxISGJCiPDGuNXflq4BiqJwxcHxH/GRErdtt2SF1Vpl0vDC8Xjbe+gMY+8mus1hfdwE6/SJL6NqFj3ww85k1G+5jV3c763ds2v9449fV1vPSvgOHQGupnouNGPpXqD4prM/NogIj4NHAy8H6qH6gPDbqR0+i3Zdrrl2PjtvnLx6lv1TKd6Gl7SdLgTfTYN9OOeZNxQdv8yl3KtD8quuv410Vfx0tD5xDIzHsj4m9M/MaW1mmGB5/gkJn3RcRxZd3OreURMY/qzr7/Hkxrp+VOvl+U6foRsUZm/qNDmdaddYuprvnp5RFleuPEmyhJGqQ+jn0z7Zg3GedRhfk5VHeyd+qEmdU2/4dx6uvreGnoHB5XAU+KiBUyc7y7r59epmeMWb5BW10tL+GhXekARMTKwH9Q/bLfTtUNPz8zfz5eQ6f6Tr7MvDgiWvt8BnBch2L/VKZnZeYtHda3a/07XT2YFkqSaprIsW9GHfMmIzPviYgfUD3Pfgug0+d7TNv8MeNU2dfx0ms6h8cCqv+vTXoVioi1WDpI69hfwNZfJJe0LXspcGyHqg4HrsvMf87M91INKPuRfhs9hb5WpgeNXVEez/Xi8vaTE6hrXpn6GDVJml49j30z+Jg3GR+nGjqw2zPTn1Gm383M8S5Hm1emEzpeGjqHxzllum3PUvA0qusxLsvMG8as+2mZrgYQEa8ETsvMh9z9HRGrA6/gob+ovwQ+VqPdU+ULwKXAPhHx7DHr/h1YAzgyM7sNEdFuG6qxzH49yAZKkvo23rFvph7zAIjKE1j6jPp1I2Lv0nPbUWZeRnWN694R8awx9W1KNb7pZcBbJ9CEbejjeOnp9eHxM6pB0Heh819pLfdSDQn08bErMvMnEfEvwFsi4nnAhcA7O9RxP9WphZMi4kfAWcDRmTne+JbTply/swfwXeB7EfEl4I9UF5jvRzU22+snWN0OwPnjDIgrSWreeMe+GXnMi4idgR9QjY/Zfg3mysBJwOKIuBX4dma+a+z2mfnRiFgB+H5EHE71bPt5wGuowvobMnO8m4igz+NlZOb4pbRMiIizgUdl5hOnYF9PBz4BbAcsT/UX4K7DEMQi4v9R/SI8imq8sTMzc0JPc4qIzah6TN+TmZ9urpWSpImYimPfMB/zJiMiHgPsBaxN9UjLX2bmHye4bd/HS0PnEImIA4AjgK0zc0quNyyPIXsdcBjwsszsdJPOyIiI9wIfBTbOzOumuz2SNNNN5bFvph3zJqPO8dJrOofLd4HrqK63aEREfDYiri+/eGTmXWVoiVt56KCyo+q1wHEGTklaZjR27POYNyl9Hy8NnUOkPEP2A8BrIuJRDe1mZ+Bclj4Jgoh4I3BqZl7Y0D6XCeUGpE2A/5zutkiSKg0f+2bsMW8y6h4vPb0+ZCIiqMbV+m1mvqWB+jcHXkZ1k9ksqru+fwt8aQLjgw6tiFgO+A3VuGz/Ms3NkSS1aerYN1OPeZMxmeOloXMIRcRGVMMT7JuZ4z1dRxMQEe+iembvzsvyHYuSNFN57Fs2TOZ4aegcUhHxNODrwNO9/nByIuJJwA+B3Sd6l7skaep57Jtekz1eGjqHWEQ8BXhfZo4dDF0TFBEbAD8BXjVVIwJIkurz2Dc9BnG8NHRqRitPopidmTdOd1skSVpWDeJ4aeiUJElS4xwySZIkSY0zdEqSJKlxhk5JkiQ1ztApSZKkxhk6JUmS1DhDpyRJkhpn6JQkSVLj/j+mpaAJx973IwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tIterating over graph EV\n",
      "\t\tReading file k_core-gender-net_EV-data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### PLOTTING THE RESULTS ####\n",
    "\n",
    "# setting common variables\n",
    "\n",
    "barwidth = 1.\n",
    "\n",
    "#### ITERATING OVER GRAPH TYPES #####\n",
    "\n",
    "for i in range(nr_graphs):\n",
    "    \n",
    "    #myG = graph_list[i]\n",
    "    mylabel = label_list[i]\n",
    "\n",
    "    print(\"\\tIterating over graph %s\" %(mylabel))\n",
    "\n",
    "    fig = plt.figure(figsize=(10,10))\n",
    "    fig.subplots(1, 1)\n",
    "\n",
    "    ### SUBPLOT 1 (GENDER FRACTION PER K-SHELL)\n",
    "\n",
    "    ax1 = plt.subplot(111)\n",
    "\n",
    "\n",
    "    # extracting the data\n",
    "\n",
    "    labelx = list()\n",
    "    valx = list()\n",
    "    valy_m = list()\n",
    "    valy_f = list()\n",
    "    valy_o = list()\n",
    "\n",
    "    count = 0\n",
    "\n",
    "    # reading the input file\n",
    "    \n",
    "    fnamein = 'k_core-gender-net_'+mylabel+'-data.dat'\n",
    "    \n",
    "    print('\\t\\tReading file %s ...\\n' %(fnamein))\n",
    "\n",
    "    ks_gender_density = np.loadtxt(diroutname+fnamein, delimiter=' ', dtype=float)\n",
    "  \n",
    "    \n",
    "    for i in range(len(ks_gender_density)):\n",
    "        mysum = sum(ks_gender_density[i,2:])\n",
    "\n",
    "        if mysum > 0.:\n",
    "            valx.append(count)\n",
    "            labelx.append(repr(int(ks_gender_density[i][1])))\n",
    "            valy_m.append(ks_gender_density[i][2])\n",
    "            valy_f.append(ks_gender_density[i][3])\n",
    "            valy_o.append(ks_gender_density[i][4])\n",
    "            count += 1\n",
    "\n",
    "\n",
    "    np.asarray(valx)    \n",
    "    np.asarray(valy_m)\n",
    "    np.asarray(valy_f)\n",
    "    np.asarray(valy_o)\n",
    "            \n",
    "    # plotting\n",
    "\n",
    "    ax1.bar(valx, valy_o, width=barwidth, label='Unknown', color=mycolor_unkno)\n",
    "    ax1.bar(valx, valy_m, width=barwidth, label='Men', color=mycolor_men,\\\n",
    "           bottom=valy_o)\n",
    "    ax1.bar(valx, valy_f, width=barwidth, label='Women', color=mycolor_women,\\\n",
    "           bottom=np.add(valy_o, valy_m) )\n",
    "\n",
    "\n",
    "    ## setting axes features\n",
    "\n",
    "    #ax1.set_ylim([-0.025,1.025])\n",
    "    ax1.set_ylim([0.,1.0])\n",
    "    ax1.set_xlim([-0.5*barwidth,len(valx)-(0.5*barwidth)])\n",
    "\n",
    "    #xticksval = valx[::2]+[valx[-1]]\n",
    "    #xtickslab = labelx[::2]+[labelx[-1]]\n",
    "\n",
    "    xticksval = [valx[0],valx[-1]]\n",
    "    xtickslab = [r'$\\;\\;\\;(k_s = '+labelx[0]+')$',r'$\\!\\!\\!\\!\\!(k_s = '+labelx[-1]+')$']\n",
    "\n",
    "    ax1.set_xticks(xticksval)\n",
    "    ax1.set_xticklabels(xtickslab)\n",
    "\n",
    "    ax1.tick_params(axis='y', which='major', labelsize=25)\n",
    "    ax1.tick_params(axis='x', which='major', labelsize=30)\n",
    "    ax1.tick_params(axis='x', pad=40)\n",
    "    ax1.tick_params(axis='y', pad=10)\n",
    "\n",
    "    ax1.set_ylabel('Fraction of authors', fontsize=28, labelpad=10)\n",
    "    #ax1.set_xlabel(r'$k_s$', fontsize=30, labelpad=30)\n",
    "\n",
    "    # textboxes\n",
    "\n",
    "    ax1.text(0.05, -0.025, 'outer', transform=ax1.transAxes, fontsize=25, va='top', ha='center')\n",
    "    ax1.text(0.95, -0.025, 'inner', transform=ax1.transAxes, fontsize=25, va='top', ha='center')\n",
    "\n",
    "    ax1.annotate('', xy=(0.85, -0.05), xycoords='axes fraction', xytext=(0.15, -0.05),\\\n",
    "                 arrowprops=dict(width=0.15, color='k', linewidth=3.5))\n",
    "\n",
    "\n",
    "\n",
    "    ax1.spines['right'].set_visible(False)\n",
    "    ax1.spines['top'].set_visible(False)\n",
    "\n",
    "\n",
    "\n",
    "    # legend\n",
    "\n",
    "    #ax2.legend(bbox_to_anchor=(1.05, 0.6),fontsize=20)\n",
    "    ax1.legend(loc=(0.03, 1.01),fontsize=22, ncol=3, frameon=False)\n",
    "\n",
    "    diroutname = WD[:-4]+'results/'\n",
    "    fignameout = 'k_core-gender-net_'+mylabel+'.pdf'\n",
    "\n",
    "    plt.savefig(diroutname+fignameout, bbox_inches='tight')\n",
    "\n",
    "    plt.show()            "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc0900ab",
   "metadata": {},
   "source": [
    "### Visualization aleatoric -- standalone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9f70550a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tIterating over graph REL\n",
      "\t\tReading file k_core-gender-net_REL-rand_nrep-50-data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tIterating over graph COR\n",
      "\t\tReading file k_core-gender-net_COR-rand_nrep-50-data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tIterating over graph CB\n",
      "\t\tReading file k_core-gender-net_CB-rand_nrep-50-data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tIterating over graph PUB\n",
      "\t\tReading file k_core-gender-net_PUB-rand_nrep-50-data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tIterating over graph EV\n",
      "\t\tReading file k_core-gender-net_EV-rand_nrep-50-data.dat ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### PLOTTING THE RESULTS ####\n",
    "\n",
    "# setting common variables\n",
    "\n",
    "barwidth = 1.\n",
    "\n",
    "#### ITERATING OVER GRAPH TYPES #####\n",
    "\n",
    "for i in range(nr_graphs):\n",
    "    \n",
    "    #myG = graph_list[i]\n",
    "    mylabel = label_list[i]\n",
    "\n",
    "    print(\"\\tIterating over graph %s\" %(mylabel))\n",
    "\n",
    "    fig = plt.figure(figsize=(10,10))\n",
    "    fig.subplots(1, 1)\n",
    "\n",
    "    ### SUBPLOT 1 (GENDER FRACTION PER K-SHELL)\n",
    "\n",
    "    ax1 = plt.subplot(111)\n",
    "\n",
    "\n",
    "    # extracting the data\n",
    "\n",
    "    labelx = list()\n",
    "    valx = list()\n",
    "    valy_m = list()\n",
    "    valy_f = list()\n",
    "    valy_o = list()\n",
    "\n",
    "    count = 0\n",
    "\n",
    "    # reading the input file\n",
    "    \n",
    "    fnamein = 'k_core-gender-net_'+mylabel+'-rand_nrep-'+repr(nr_replicas)+'-data.dat'\n",
    "    \n",
    "    print('\\t\\tReading file %s ...\\n' %(fnamein))\n",
    "\n",
    "    ks_gender_density_rand = np.loadtxt(diroutname+fnamein, delimiter=' ', dtype=float)\n",
    "\n",
    "    for i in range(len(ks_gender_density_rand)):\n",
    "        mysum = sum(ks_gender_density_rand[i,2:])\n",
    "\n",
    "        if mysum > 0.:\n",
    "            valx.append(count)\n",
    "            labelx.append(repr(int(ks_gender_density_rand[i][1])))\n",
    "            valy_m.append(ks_gender_density_rand[i][2])\n",
    "            valy_f.append(ks_gender_density_rand[i][3])\n",
    "            valy_o.append(ks_gender_density_rand[i][4])\n",
    "            count += 1\n",
    "\n",
    "    np.asarray(valx)    \n",
    "    np.asarray(valy_m)\n",
    "    np.asarray(valy_f)\n",
    "    np.asarray(valy_o)\n",
    "\n",
    "    # plotting\n",
    "\n",
    "    ax1.bar(valx, valy_o, width=barwidth, label='Unknown', color=mycolor_unkno)\n",
    "    ax1.bar(valx, valy_m, width=barwidth, label='Men', color=mycolor_men,\\\n",
    "           bottom=valy_o)\n",
    "    ax1.bar(valx, valy_f, width=barwidth, label='Women', color=mycolor_women,\\\n",
    "           bottom=np.add(valy_o, valy_m) )\n",
    "\n",
    "\n",
    "    ## setting axes features\n",
    "\n",
    "    #ax1.set_ylim([-0.025,1.025])\n",
    "    ax1.set_ylim([0.,1.0])\n",
    "    ax1.set_xlim([-0.5*barwidth,len(valx)-(0.5*barwidth)])\n",
    "\n",
    "    #xticksval = valx[::2]+[valx[-1]]\n",
    "    #xtickslab = labelx[::2]+[labelx[-1]]\n",
    "\n",
    "    xticksval = [valx[0],valx[-1]]\n",
    "    xtickslab = [r'$\\;\\;\\;(k_s = '+labelx[0]+')$',r'$\\!\\!\\!\\!\\!(k_s = '+labelx[-1]+')$']\n",
    "\n",
    "    ax1.set_xticks(xticksval)\n",
    "    ax1.set_xticklabels(xtickslab)\n",
    "\n",
    "    ax1.tick_params(axis='y', which='major', labelsize=25)\n",
    "    ax1.tick_params(axis='x', which='major', labelsize=30)\n",
    "    ax1.tick_params(axis='x', pad=40)\n",
    "    ax1.tick_params(axis='y', pad=10)\n",
    "\n",
    "    ax1.set_ylabel('Fraction of authors', fontsize=28, labelpad=10)\n",
    "    #ax1.set_xlabel(r'$k_s$', fontsize=30, labelpad=30)\n",
    "\n",
    "    # textboxes\n",
    "\n",
    "    ax1.text(0.05, -0.025, 'outer', transform=ax1.transAxes, fontsize=25, va='top', ha='center')\n",
    "    ax1.text(0.95, -0.025, 'inner', transform=ax1.transAxes, fontsize=25, va='top', ha='center')\n",
    "\n",
    "    ax1.annotate('', xy=(0.85, -0.05), xycoords='axes fraction', xytext=(0.15, -0.05),\\\n",
    "                 arrowprops=dict(width=0.15, color='k', linewidth=3.5))\n",
    "\n",
    "\n",
    "\n",
    "    ax1.spines['right'].set_visible(False)\n",
    "    ax1.spines['top'].set_visible(False)\n",
    "\n",
    "\n",
    "\n",
    "    # legend\n",
    "\n",
    "    #ax2.legend(bbox_to_anchor=(1.05, 0.6),fontsize=20)\n",
    "    ax1.legend(loc=(0.03, 1.01),fontsize=22, ncol=3, frameon=False)\n",
    "\n",
    "    diroutname = WD[:-4]+'results/'\n",
    "    fignameout = 'k_core-gender-net_'+mylabel+'-rand.pdf'\n",
    "\n",
    "    plt.savefig(diroutname+fignameout, bbox_inches='tight')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be5ee284",
   "metadata": {},
   "source": [
    "### Visualization lineplots -- all together"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf1c987e",
   "metadata": {},
   "source": [
    "#### Data acquisition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "90f9c5d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tIterating over graph REL\n",
      "\t\tReading files:\n",
      "\t\tk_core-gender-net_REL-data.dat\n",
      "\n",
      "\t\tand\n",
      "\n",
      "\t\tk_core-gender-net_REL-rand_nrep-50-data.dat\n",
      "\n",
      "\tIterating over graph COR\n",
      "\t\tReading files:\n",
      "\t\tk_core-gender-net_COR-data.dat\n",
      "\n",
      "\t\tand\n",
      "\n",
      "\t\tk_core-gender-net_COR-rand_nrep-50-data.dat\n",
      "\n",
      "\tIterating over graph CB\n",
      "\t\tReading files:\n",
      "\t\tk_core-gender-net_CB-data.dat\n",
      "\n",
      "\t\tand\n",
      "\n",
      "\t\tk_core-gender-net_CB-rand_nrep-50-data.dat\n",
      "\n",
      "\tIterating over graph PUB\n",
      "\t\tReading files:\n",
      "\t\tk_core-gender-net_PUB-data.dat\n",
      "\n",
      "\t\tand\n",
      "\n",
      "\t\tk_core-gender-net_PUB-rand_nrep-50-data.dat\n",
      "\n",
      "\tIterating over graph EV\n",
      "\t\tReading files:\n",
      "\t\tk_core-gender-net_EV-data.dat\n",
      "\n",
      "\t\tand\n",
      "\n",
      "\t\tk_core-gender-net_EV-rand_nrep-50-data.dat\n",
      "\n",
      "\tIterating over graph ALL\n",
      "\t\tReading files:\n",
      "\t\tk_core-gender-data.dat\n",
      "\n",
      "\t\tand\n",
      "\n",
      "\t\tk_core-gender-rand_nrep-50-data.dat\n",
      "\n",
      "End of data acquisition\n"
     ]
    }
   ],
   "source": [
    "# initializing data\n",
    "ks_data_diff = {}\n",
    "\n",
    "# iterating over graphs\n",
    "for i in range(nr_graphs):\n",
    "\n",
    "    mylabel = label_list[i]\n",
    "    \n",
    "    ks_data_diff[mylabel] = {}\n",
    "    \n",
    "    print(\"\\tIterating over graph %s\" %(mylabel))\n",
    "    \n",
    "    # reading the input files\n",
    "    \n",
    "    fnamein = 'k_core-gender-net_'+mylabel+'-data.dat'\n",
    "    fnamein_rand = 'k_core-gender-net_'+mylabel+'-rand_nrep-'+\\\n",
    "                   repr(nr_replicas)+'-data.dat'\n",
    "    \n",
    "    print('\\t\\tReading files:\\n\\t\\t%s\\n\\n\\t\\tand\\n\\n\\t\\t%s\\n' %(fnamein, fnamein_rand))\n",
    "\n",
    "    ks_data = np.loadtxt(diroutname+fnamein, delimiter=' ',\\\n",
    "                         usecols=(1,3), dtype=float)\n",
    "    ks_data_rand = np.loadtxt(diroutname+fnamein_rand, delimiter=' ',\\\n",
    "                              usecols=(1,3), dtype=float)\n",
    "    \n",
    "    # max val of k_s index (both empiric and random)\n",
    "    \n",
    "    max_ks = int(max([ks_data[-1][0],ks_data_rand[-1][0]]))\n",
    "    \n",
    "    # initializing data structure\n",
    "    \n",
    "    dummy = np.zeros((max_ks+1,2))\n",
    "    \n",
    "    for i in range(len(dummy)):\n",
    "        dummy[i][0] = i\n",
    "    \n",
    "    # filling data structure (empiric)\n",
    "    \n",
    "    for i in range(len(ks_data)):\n",
    "        v = int(ks_data[i][0])\n",
    "        \n",
    "        dummy[v][1] = ks_data[i][1]\n",
    "\n",
    "    # filling data structure (random)\n",
    "    \n",
    "    for i in range(len(ks_data_rand)):\n",
    "        v = int(ks_data_rand[i][0])\n",
    "        \n",
    "        dummy[v][1] -= ks_data_rand[i][1]\n",
    "        \n",
    "    # trasferimento su struttura dati finale\n",
    "    v = int(ks_data_rand[-1][0])\n",
    "    ks_data_diff[mylabel]['data'] = cp.copy(dummy)\n",
    "    ks_data_diff[mylabel]['rextreme'] = (v,dummy[v][1])\n",
    "    \n",
    "# La complete network la faccio a parte\n",
    "\n",
    "mylabel = 'ALL'\n",
    "\n",
    "ks_data_diff[mylabel] = {}\n",
    "\n",
    "print(\"\\tIterating over graph %s\" %(mylabel))\n",
    "\n",
    "# reading the input files\n",
    "\n",
    "fnamein = 'k_core-gender-data.dat'\n",
    "fnamein_rand = 'k_core-gender-rand_nrep-'+repr(nr_replicas)+'-data.dat'\n",
    "\n",
    "print('\\t\\tReading files:\\n\\t\\t%s\\n\\n\\t\\tand\\n\\n\\t\\t%s\\n' %(fnamein, fnamein_rand))\n",
    "\n",
    "ks_data = np.loadtxt(diroutname+fnamein, delimiter=' ',\\\n",
    "                     usecols=(1,3), dtype=float)\n",
    "ks_data_rand = np.loadtxt(diroutname+fnamein_rand, delimiter=' ',\\\n",
    "                          usecols=(1,3), dtype=float)\n",
    "\n",
    "# max val of k_s index (both empiric and random)\n",
    "\n",
    "max_ks = int(max([ks_data[-1][0],ks_data_rand[-1][0]]))\n",
    "\n",
    "# initializing data structure\n",
    "\n",
    "dummy = np.zeros((max_ks+1,2))\n",
    "\n",
    "for i in range(len(dummy)):\n",
    "    dummy[i][0] = i\n",
    "\n",
    "# filling data structure (empiric)\n",
    "\n",
    "for i in range(len(ks_data)):\n",
    "    v = int(ks_data[i][0])\n",
    "\n",
    "    dummy[v][1] = ks_data[i][1]\n",
    "\n",
    "# filling data structure (random)\n",
    "\n",
    "for i in range(len(ks_data_rand)):\n",
    "    v = int(ks_data_rand[i][0])\n",
    "\n",
    "    dummy[v][1] -= ks_data_rand[i][1]\n",
    "\n",
    "# trasferimento su struttura dati finale\n",
    "v = int(ks_data_rand[-1][0])\n",
    "ks_data_diff[mylabel]['data'] = cp.copy(dummy)\n",
    "ks_data_diff[mylabel]['rextreme'] = (v,dummy[v][1])\n",
    "\n",
    "\n",
    "print('End of data acquisition')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d762607e",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "10c1af1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_llist = ['ALL', 'REL', 'COR', 'CB', 'PUB', 'EV' ]\n",
    "vx_max = {'ALL': (-5, 60),\\\n",
    "          'EV': (-5, 60),\\\n",
    "          'PUB': (-1, 17),\\\n",
    "          'CB': (-1, 21),\\\n",
    "          'COR': (-0.5, 3.5),\\\n",
    "          'REL': (-0.5, 4.5)}\n",
    "\n",
    "max_locat = {'ALL': 20,\\\n",
    "             'EV': 20,\\\n",
    "             'PUB': 4,\\\n",
    "             'CB': 4,\\\n",
    "             'COR': 1,\\\n",
    "             'REL': 2}\n",
    "\n",
    "min_locat = {'ALL': 10,\\\n",
    "             'EV': 10,\\\n",
    "             'PUB': 2,\\\n",
    "             'CB': 2,\\\n",
    "             'COR': 1,\\\n",
    "             'REL': 1}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# PLOTTING THE DATA\n",
    "\n",
    "fig = plt.subplots(nrows=2,ncols=3,figsize=(15,9))\n",
    "\n",
    "icount = 1\n",
    "\n",
    "for lab in my_llist:\n",
    "    \n",
    "    substring=230+icount\n",
    "    ax = plt.subplot(substring)\n",
    "    \n",
    "    #mycol = mycustom_colors2['mycol2'+repr(icount)]\n",
    "    mycol = '#003049'\n",
    "    \n",
    "    px = ks_data_diff[lab]['rextreme'][0]\n",
    "    py = ks_data_diff[lab]['rextreme'][1]\n",
    "    \n",
    "    ppos = np.where(ks_data_diff[lab]['data'][:,0] == px)[0][0]\n",
    "    \n",
    "    dummy1 = ks_data_diff[lab]['data'][:,0]\n",
    "    dummy2 = ks_data_diff[lab]['data'][:,1]\n",
    "\n",
    "    # splitto i dati in due per disegnarli in modo diverso\n",
    "    \n",
    "    vx1 = cp.copy(dummy1[:ppos+1])\n",
    "    vy1 = cp.copy(dummy2[:ppos+1])\n",
    "\n",
    "    vx2 = cp.copy(dummy1[ppos:])\n",
    "    vy2 = cp.copy(dummy2[ppos:])\n",
    "\n",
    "    \n",
    "    ax.plot(vx1,vy1, label=lab, color=mycol, lw=2, zorder=2)\n",
    "    ax.plot(vx2,vy2, label=lab, color=mycol, lw=2, dashes=(6,3), zorder=2)\n",
    "    \n",
    "    # star point\n",
    "    ax.scatter(px,py, marker='*', s=350 , c='#d62828', edgecolor='w', zorder=3)\n",
    "    \n",
    "    # horizontal dashed line\n",
    "    ax.plot([-5,100],[0.,0.],ls='-',color='silver',dashes=(10,5),zorder=1)\n",
    "\n",
    "    # axis limits\n",
    "    ax.set_xlim([vx_max[lab][0],vx_max[lab][1]])\n",
    "    ax.set_ylim([-0.7,0.45])\n",
    "\n",
    "    # axis labels\n",
    "    \n",
    "    if icount == 1:\n",
    "        ax.set_ylabel(r'$f_{\\rm w}^e - f_{\\rm w}^r$', labelpad=10, fontsize=35)\n",
    "    elif icount == 4:\n",
    "        ax.set_ylabel(r'$f_{\\rm w}^e - f_{\\rm w}^r$', labelpad=10, fontsize=35)\n",
    "        ax.set_xlabel(r'$k_s$', fontsize=35)\n",
    "    elif icount in [5,6]:\n",
    "        ax.set_xlabel(r'$k_s$', fontsize=35)\n",
    "        \n",
    "    # major/minor locator\n",
    "    \n",
    "    ax.xaxis.set_major_locator(MultipleLocator(max_locat[lab]))\n",
    "    ax.xaxis.set_minor_locator(MultipleLocator(min_locat[lab]))\n",
    "      \n",
    "    # tick parameters\n",
    "    ax.xaxis.set_tick_params(length=12,labelsize=18,pad=10)\n",
    "    ax.yaxis.set_tick_params(length=12,labelsize=18,pad=5)\n",
    "    \n",
    "    ax.xaxis.set_tick_params(which='minor', length=8)\n",
    "\n",
    "    # spines\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "\n",
    "    # graph label\n",
    "    ax.text(0.8, 0.95, lab, transform=ax.transAxes, fontsize=25,\\\n",
    "            va='top', ha='center', color='grey')\n",
    "    \n",
    "    \n",
    "    # counter update\n",
    "    icount += 1\n",
    "\n",
    "    \n",
    "diroutname = WD[:-4]+'results/'\n",
    "fignameout = 'k_core-woman-diff_comparison.pdf'\n",
    "\n",
    "plt.subplots_adjust(hspace=0.2,wspace=0.3)\n",
    "\n",
    "plt.savefig(diroutname+fignameout, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95dc1433",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
